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  • View in gallery

    (a) Topography and (b) valley-floor surface of the Austrian INCA domain.

  • View in gallery

    North–south topographic cross sections in a band of 20-km width in the middle of the domain (near 13.5°E). Also shown are the valley-floor surface (boldface) and the z-coordinate surfaces (dashed).

  • View in gallery

    Schematic representation of the coordinate system used in the INCA wind analysis, showing “shaved elements” (subterranean parts dotted) generated by the intersection of z surfaces with the terrain (boldface line).

  • View in gallery

    Stations used operationally in the hourly temperature and humidity analysis [filled circles represent TAWES and surface synoptic observation (SYNOP) stations; open circles represent hydrological stations].

  • View in gallery

    INCA surface-layer index as computed by (1). Values greater than 0.9 characterize valley and basin floors, and generally flat or hilly terrain. Values less than 0.1 characterize elevated mountain slopes, ridges, and peaks.

  • View in gallery

    In parts of a valley atmosphere not represented in the NWP model, the first-guess temperature profile is constructed by performing a downward shift of the PBL temperature profile to the level of the valley-floor surface.

  • View in gallery

    Example of an INCA temperature analysis during a typical mixed foehn–inversion situation at 0800 UTC 21 Nov 2007. Cold-air pools are present in valleys and basins, while foehn-induced subsidence and mixing create high temperatures at elevations between 1500 and 2000 m.

  • View in gallery

    Example of a 15-min INCA precipitation analysis based on the combination of rain gauge and radar data (1930 UTC 19 Jun 2009): (top left) pure rain gauge interpolation, (top right) uncorrected max-CAPPI radar composite, (bottom left) corrected radar field, and (bottom right) final INCA precipitation analysis. Rain gauge locations are shown by black dots, and locations of radar stations are indicated by red triangles.

  • View in gallery

    NWP filtering of motion vectors according to (30). The boldface vector is the NWP model wind, and black arrows show examples of permitted motion vectors. Vectors outside the elliptic area are rejected. The dotted line shows the wind speed deviation parameter, Δ.

  • View in gallery

    Distribution of individual analysis–observation pairs in the INCA temperature cross validation for July 2009. For better legibility only data from every third analysis (0000, 0300, … , 2100 UTC) are shown.

  • View in gallery

    Distribution of individual analysis–observation pairs in the INCA 15-min precipitation cross validation for July 2009. For better legibility only data from every 12th analysis (0000, 0300, … , 2100 UTC) have been included in the plot. The line pattern is due to the finite resolution (0.1 mm) of the rain gauge data. Only values ≥0.1 mm are shown.

  • View in gallery

    INCA (dashed lines) and ALADIN (solid lines) MAEs of temperature, averaged over all stations, for July 2009 (gray) and January 2010 (black).

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The Integrated Nowcasting through Comprehensive Analysis (INCA) System and Its Validation over the Eastern Alpine Region

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  • 1 Central Institute for Meteorology and Geodynamics, Vienna, Austria
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Abstract

This paper presents the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which has been developed for use in mountainous terrain. Analysis and nowcasting fields include temperature, humidity, wind, precipitation amount, precipitation type, cloudiness, and global radiation. The analysis part of the system combines surface station data with remote sensing data in such a way that the observations at the station locations are reproduced, whereas the remote sensing data provide the spatial structure for the interpolation. The nowcasting part employs classical correlation-based motion vectors derived from previous consecutive analyses. In the case of precipitation the nowcast includes an intensity-dependent elevation effect. After 2–6 h of forecast time the nowcast is merged into an NWP forecast provided by a limited-area model, using a predefined temporal weighting function. Cross validation of the analysis and verification of the nowcast are performed. Analysis quality is high for temperature, but comparatively low for wind and precipitation, because of the limited representativeness of station data in mountainous terrain, which can be only partially compensated by the analysis algorithm. Significant added value of the system compared to the NWP forecast is found in the first few hours of the nowcast. At longer lead times the effects of the latest observations becomes small, but in the case of temperature the downscaling of the NWP forecast within the INCA system continues to provide some improvement compared to the direct NWP output.

Corresponding author address: Thomas Haiden, Central Institute for Meteorology and Geodynamics, Hohe Warte 38, A-1190 Vienna, Austria. E-mail: thomas.haiden@zamg.ac.at

Abstract

This paper presents the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which has been developed for use in mountainous terrain. Analysis and nowcasting fields include temperature, humidity, wind, precipitation amount, precipitation type, cloudiness, and global radiation. The analysis part of the system combines surface station data with remote sensing data in such a way that the observations at the station locations are reproduced, whereas the remote sensing data provide the spatial structure for the interpolation. The nowcasting part employs classical correlation-based motion vectors derived from previous consecutive analyses. In the case of precipitation the nowcast includes an intensity-dependent elevation effect. After 2–6 h of forecast time the nowcast is merged into an NWP forecast provided by a limited-area model, using a predefined temporal weighting function. Cross validation of the analysis and verification of the nowcast are performed. Analysis quality is high for temperature, but comparatively low for wind and precipitation, because of the limited representativeness of station data in mountainous terrain, which can be only partially compensated by the analysis algorithm. Significant added value of the system compared to the NWP forecast is found in the first few hours of the nowcast. At longer lead times the effects of the latest observations becomes small, but in the case of temperature the downscaling of the NWP forecast within the INCA system continues to provide some improvement compared to the direct NWP output.

Corresponding author address: Thomas Haiden, Central Institute for Meteorology and Geodynamics, Hohe Warte 38, A-1190 Vienna, Austria. E-mail: thomas.haiden@zamg.ac.at

1. Introduction

There has been remarkable improvement in the quality of numerical weather prediction (NWP) model output on the global, regional, and local scales in the past three decades. For example, the skill of upper-air forecasts of the global European Centre for Medium-Range Weather Forecasts (ECMWF) model has increased by more than 3 days within this period (Richardson et al. 2009). Limited-area models with grid spacings between 2 and 20 km provide additional skill, especially with regard to precipitation and wind forecasts. A problem for NWP at all scales, however, is the comparatively low skill within the nowcasting range (0–6 h), although recent developments based on variational analysis and latent heat nudging appear promising (Dixon et al. 2009; Zhao et al. 2009).

Nowcasting methods are capable of incorporating observations almost in real time, make use of the Eulerian or Lagrangian persistence of atmospheric processes, and are computationally efficient. They are superior to NWP models, typically up to a lead time of 2–3 h (Bowler et al. 2006, Pinto et al. 2010). Nowcasting algorithms are not derived from first principles like the dynamical equations of an NWP model, which makes it more difficult to extend them beyond the classical advection nowcast. Nevertheless, because of their near-real-time availability, nowcasts have become important in time-critical applications such as severe weather warnings, flood forecasting (Werner and Cranston 2009), or aviation forecasts (Pinto et al. 2010).

Most nowcasting systems focus on precipitation and on phenomena related to deep convection (Dixon and Wiener 1993; Hand 1996; Golding 1998; Pierce et al. 2000; Bowler et al. 2006; Feng et al. 2007; Schmeits et al. 2008; Dance et al. 2010). Analysis and nowcasting of near-surface temperature (Sun and Crook 2001) or wind (Crook and Sun 2004) have mainly been regarded as a means for predicting convective initiation and the development of deep convection (Wilson and Schreiber 1986; Wilson et al. 2004). There is, however, an increasing requirement for “nonclassical” nowcasts of quantities like temperature, humidity, or global radiation. There is also a need to extend the applicability of nowcasting methods to mountainous terrain. The Integrated Nowcasting through Comprehensive Analysis (INCA) system described in this paper is an attempt to reach these goals. The basic concept is to complement and improve upon NWP direct model output for a range of variables, using real-time observations and high-resolution topographic data. Outside the immediate nowcasting range the nowcasts are blended into downscaled NWP forecasts.

Since a large number of nowcasting systems have been developed in recent years [see Dance et al. (2010) for a brief summary], one may question the need for a new system. However, the need to provide nowcasts in a highly mountainous area like the Alps required the development of new methods. In the case of precipitation, for example, the INCA methodology is useful in areas where radar coverage is poor and/or inhomogeneous but station density is good. Some aspects of the INCA system have been previously documented. Steinheimer and Haiden (2007) investigated the benefits of using high-resolution analysis fields such as convective available potential energy (CAPE) and convective inhibition (CIN) for predicting intensity changes of convective precipitation. Kann et al. (2009) used the INCA method to calibrate NWP ensemble forecasts. The objective of this paper is to provide a more comprehensive description of the system and to present new methods for the treatment of orographic effects in nowcasting. Section 2 gives a brief overview of the main characteristics of the system and the input data used. The analysis part of INCA, which employs new methods of dealing with orographic effects at high spatial resolution, is described in section 3. A radar–rain gauge combination method that accounts for the inhomogeneous radar coverage in the eastern Alps is presented. Nowcasting methods are presented in section 4, and some verification results with regard to both analyses and forecasts are given in section 5.

2. The INCA system

a. General characteristics

INCA is a multivariable analysis and nowcasting system. It provides near-real-time analyses and forecasts of the fields given in Table 1. Its objective is to improve numerical forecast products in the nowcasting (0–4 h) and very short (up to about 12 h) ranges. It also adds value to NWP forecasts for up to 72 h through downscaling and bias correction. Spatial interpolation is based on distance weighting both in physical and variable space (potential temperature, precipitation). In the case of temperature, humidity, and wind, nowcasts start with a three-dimensional analysis based on a first guess obtained from NWP output, with observation corrections superimposed. For other fields the nowcast starts with an analysis that combines remote sensing and surface station data.

Table 1.

INCA analysis and forecast fields, their input, and their update frequency: NWP = output of an NWP model, SFC = surface station observations, RAD = radar data, and SAT = satellite data. Two-letter acronyms indicate INCA fields.

Table 1.

There is limited interdependency between the fields. In the nowcasting of temperature the cloudiness analysis and nowcast are taken into account. The surface cooling caused by convective cells due to the evaporation of precipitation enters the analysis and nowcasting of temperature. Additional, derived fields include convective parameters such as the lifted condensation level (LCL), or CAPE, as described by Steinheimer and Haiden (2007). Snowfall line and ground temperature are computed for nowcasts of precipitation type (snow, rain, snow–rain mix, freezing rain).

The topography is constructed from a bilinear interpolation to the 1-km INCA grid of the 30″ digital elevation dataset provided by the U.S. Geological Survey. No smoothing other than the one implicit in the bilinear interpolation is applied. The high resolution of 1 km is an essential characteristic of INCA. It enables the system to directly assimilate observations at most of the stations, since at this resolution the real elevation and exposition of a location come close to that given by the topography on the numerical grid. It allows us to resolve major Alpine valleys such that the modeled valley floor is close to the actual valley floor height. It was subjectively concluded that the resolution is sufficient to approximately reproduce slope inclinations on major valley sidewalls. In the Alps, the typical base-to-crest length scale is of the order of 5 km, which means that sidewall slopes extend over several grid intervals.

The main conceptual difference between the analysis part of INCA and the Austrian Vienna Enhanced Resolution Analysis system (VERA; Steinacker et al. 2006; Schneider et al. 2008) is that INCA relies on NWP model output and high-resolution remote sensing data to interpolate between observations, while VERA is model independent and based on the variational principle applied to higher-order spatial derivatives. It uses a fingerprint technique to incorporate conceptual or climatological information, or upscaled radar data.

b. Coordinate system

Several different INCA domains exist in central Europe. In this paper we refer to the Austrian operational domain, which has a mesh size of 1 km and covers an area of 600 km × 350 km, centered over the eastern Alps (Fig. 1a). In the vertical, a z system is used where z is the height above the “valley-floor surface” shown in Fig. 2. It is a spatially slowly varying reference surface (Fig. 1b), which is smooth compared to the actual topography and connects major valley floors (Haiden 1998). It separates the topography into a base topography and a relative topography and is computed by assigning to every grid point the minimum elevation found within a radius of 10 km. The resulting field is smoothed with a running-average window of 20 km × 20 km. Over flat terrain, the topography and valley-floor surface coincide. Other nowcasting systems that include the vertical dimension are usually based more directly on an NWP model and therefore employ a terrain-following coordinate (Dixon et al. 2009). The coordinate based on the valley-floor surface is needed for the downward extrapolation of three-dimensional NWP forecast fields into valleys, and as a reference height for the parameterization of vertical profiles of temperature and precipitation.

Fig. 1.
Fig. 1.

(a) Topography and (b) valley-floor surface of the Austrian INCA domain.

Citation: Weather and Forecasting 26, 2; 10.1175/2010WAF2222451.1

Fig. 2.
Fig. 2.

North–south topographic cross sections in a band of 20-km width in the middle of the domain (near 13.5°E). Also shown are the valley-floor surface (boldface) and the z-coordinate surfaces (dashed).

Citation: Weather and Forecasting 26, 2; 10.1175/2010WAF2222451.1

The vertical grid spacing is uniform with Δz = 200 m, and 21 levels including the surface, covering the lowest 4000 m above the local valley-floor surface. The coordinate system used in the wind analysis is a z system with horizontal coordinate surfaces intersecting the terrain. There are 32 levels at a constant spacing of Δz = 125 m. The irregular shape of the grid volumes intersecting the terrain (Fig. 3) is taken into account in the computation of divergence, which is part of the relaxation procedure (Steppeler et al. 2002).

Fig. 3.
Fig. 3.

Schematic representation of the coordinate system used in the INCA wind analysis, showing “shaved elements” (subterranean parts dotted) generated by the intersection of z surfaces with the terrain (boldface line).

Citation: Weather and Forecasting 26, 2; 10.1175/2010WAF2222451.1

c. Data sources

1) NWP model output

NWP fields are provided by the Austrian operational version of the Aire Limitée Adaptation Dynamique Développement International (ALADIN) limited-area model described by Wang et al. (2006). It has a horizontal resolution of 9.6 km, 60 levels in the vertical, and is run 4 times per day with a forecast range of 72 h. Postprocessed fields are available roughly 4 h after analysis time. Forecast fields used in INCA are geopotential, temperature, relative humidity, and wind components (3D fields), as well as 2-m temperature and relative humidity, 10-m wind components, precipitation, total cloudiness, low cloudiness, and ground temperature (2D fields). The analysis and nowcasting methods of INCA are not specific to ALADIN. The Swiss version, INCA-CH, for example, uses Consortium for Small-Scale Modeling (COSMO; Steppeler et al. 2003) fields as a first guess. However, some of the empirical parameters and coefficients used in the analysis procedures are potentially sensitive to NWP model resolution and may have to be recalibrated before use in another NWP system.

2) Surface station observations

The Central Institute for Meteorology and Geodynamics (Zentralanstalt für Meteorologie und Geodynamik; ZAMG) operates a network of ∼250 Teilautomatische Wetterstationen (TAWES) semiautomated weather stations in Austria. The network covers most of the topographic elevation range (100–3800 m), with its highest stations at Brunnenkogel (3440 m) and Sonnblick (3105 m). The average horizontal distance between stations is 18 km. The vertical distribution of stations is somewhat biased toward elevations less than 2000 m but there is a sufficient number of mountain stations to allow the construction of three-dimensional correction fields to the NWP model output. Meteorological observations used are 2-m temperature, relative humidity, dewpoint, 10-m wind speed and direction, precipitation amount, and sunshine duration. In addition to TAWES stations, the Austrian network of hydrometeorological stations provides real-time precipitation and temperature data at ∼100 locations (Fig. 4).

Fig. 4.
Fig. 4.

Stations used operationally in the hourly temperature and humidity analysis [filled circles represent TAWES and surface synoptic observation (SYNOP) stations; open circles represent hydrological stations].

Citation: Weather and Forecasting 26, 2; 10.1175/2010WAF2222451.1

The following measurement methods are used, with the accuracy obtained in the laboratory given in parentheses. Temperature is measured with linearized negative temperature coefficient (NTC) thermistors (±0.1°C), and relative humidity and dewpoint with capacitive sensors (±5% at humidities <90%) and dewpoint mirrors (±0.2°C), respectively. For wind, mechanical anemometers and sonic instruments are used (±0.5 m s−1 up to 5 m s−1, ±5% above). For precipitation measurement, TAWES stations are equipped with tipping-bucket rain gauges and weighting rain gauges (±10%). Sunshine duration is obtained from heliometer measurements (±2%). In the case of precipitation, there are additional uncertainties and systematic errors due to wind effects, wetting, evaporation, and splashing, which typically amount to 5%–10% in summer, and 10%–50% in snow conditions [see Pappenberger et al. (2009) for a discussion of precipitation observation uncertainty].

3) Radar and satellite data

The radar composite used in INCA is generated from four C-band (5.33 cm) radars operated by Austria’s civil aviation authority. A fifth radar has recently been put into operation but it is not yet part of the composite. The dataset is supplemented with radar data from neighboring countries. Due to the mountainous topography, radar data quality is poor in several areas in western Austria, especially during the wintertime when much of the precipitation growth takes place at low levels. Ground clutter has already been statistically filtered when the data arrives at ZAMG. Brightband effects and range-dependent attenuation are addressed in the precipitation analysis. The maximum constant-altitude plan position indicator (max-CAPPI) product is used as the main radar input for INCA.

The Meteosat Second Generation (MSG) satellite products used in INCA are “cloud type” (Derrien and Le Gléau 2005), which consists of 17 categories, and the visible satellite image (VIS). Cloud type differentiates between three cloud levels (low, medium, and high) as well as different degrees of opaqueness. It also diagnoses whether clouds are more likely of a convective or stratiform nature. The accuracy of the cloud type product is high (93%) for low clouds, for high clouds (79%), and semitransparent clouds (92%), but rather low (37%) for midlevels clouds (Météo-France 2009). The VIS image is used to downscale the infrared-based, and thus coarser-resolution, cloud types during the day. Both satellite products are used at a 15-min time step.

d. Surface-layer index

A derived topographic field that is used in the temperature and humidity analysis and nowcast is the nondimensional surface-layer index ISFC. It characterizes the extent to which the local terrain supports the formation of a distinct surface layer. It varies between 0 and 1 and is computed as follows. For every grid point the average height of the topography within a square window of 7 × 7 points is determined from those points within the window that are at a lower elevation than the grid point in question. We denote this by , where the superscript l indicates that it is the average of the surrounding lower-elevation points only. The difference between the gridpoint elevation zH(i, j) and the average is used as a measure of how topographically exposed a location is. Higher up on a slope or at ridge locations the formation of a distinct surface layer with large values for the temperature deficit or surplus is inhibited by synoptic, mesoscale, and slope flows. Near the floor of a valley or basin a more pronounced surface layer will form. The nondimensional surface-layer index is defined as a piecewise linear function of this height difference:
e1
where the scaling parameter zS is 150 m. It represents a depth scale of Alpine cold air pools in valleys and basins and was determined indirectly by cross validation of temperature analyses computed for different values of zS. In other mountain regions a different value may have to be used. Figure 5 shows the resulting field of ISFC. It is 1 on flat terrain, valley floors, and in basins, and 0 on elevated mountain slopes, ridges, and peaks. The surface-layer index is used to limit the effects of observation corrections in the temperature and humidity fields to locations of similar type (cf. section 3a).
Fig. 5.
Fig. 5.

INCA surface-layer index as computed by (1). Values greater than 0.9 characterize valley and basin floors, and generally flat or hilly terrain. Values less than 0.1 characterize elevated mountain slopes, ridges, and peaks.

Citation: Weather and Forecasting 26, 2; 10.1175/2010WAF2222451.1

3. Analysis methods

a. Temperature and humidity

The three-dimensional analysis of temperature in INCA has been partly described by Kann et al. (2009) within the context of ensemble forecast calibration. It starts with an NWP short-range forecast as a first guess, which is then corrected based on observation–forecast differences. As the surface station observations are all made in the atmospheric surface layer, the daytime temperature surplus and nighttime temperature deficit near the surface are considered in the interpolation of these differences. The NWP forecast of 2-m temperature is separated into a 3D model-level part and a 2D surface-layer contribution:
e2
Here, TNWP is the standard NWP 2-m temperature output, and TLNWP is the temperature at the lowest model level. The difference DTNWP between the two temperatures is the temperature surplus (or deficit) in the surface layer. An analogous partitioning is made for specific humidity.
To obtain the first guess, NWP forecasts of temperature and specific humidity on pressure levels are trilinearly interpolated to the 3D INCA grid. This poses a problem in mountain valleys, where the lower parts of the valley atmosphere may not exist in the NWP model due to its coarser resolution. Thus, the boundary layer atmosphere of the NWP model is shifted downward to the INCA valley-floor surface (Fig. 6). This is done using a modified most unstable temperature gradient γ, defined here as the maximum temperature lapse rate found within the lowest layer of depth HEXT in the NWP model atmosphere over successively higher height intervals of thickness HEXT/2. After the most unstable gradient is found, it is slightly modified according to
e3
to account for the increase or decrease in stability indicated by the surface-layer contribution DTNWP. The parameter 0 ≤ fEXT ≤ 1 represents the weight given to the surface inversion in the determination of the extrapolation gradient. The most unstable gradient is used to avoid unrealistic downward extrapolations in cases of strong surface-based or elevated inversions. The parameters HEXT and fEXT have been calibrated using stations for which a downward extrapolation of at least 500 m is necessary (∼70 stations). Calibration results including both nighttime and daytime situations suggest optimum values close to HEXT = 1000 m and fEXT = 0.1. For specific humidity, an analogous downward shift is performed.
Fig. 6.
Fig. 6.

In parts of a valley atmosphere not represented in the NWP model, the first-guess temperature profile is constructed by performing a downward shift of the PBL temperature profile to the level of the valley-floor surface.

Citation: Weather and Forecasting 26, 2; 10.1175/2010WAF2222451.1

Corrections to the first guess are computed based on the differences ΔTk between the observed and NWP temperatures at station locations. Like the forecast in (2), these corrections are partitioned into a 3D model-level part and a 2D surface-layer contribution:
e4
where the subscript k denotes the value of a gridded field or observation at the kth station. The partitioning is based on the principle of minimal required correction. It is assumed that a forecast error is restricted to the surface layer as long as this is physically plausible. If the forecast error becomes too large to be explained in terms of surface-layer differences alone, part of it is classified as a vertically deeper, 3D model-level error. Following this principle, the 3D part of the temperature correction at the kth station is computed from
e5a
e5b

The parameter DTSCALE is a surface-layer temperature surplus (or deficit) scale that is by default set to a minimum value of 1 K but may have larger values depending on the insolation and wind speed. The surface-layer index ISFC in (5a) and (5b) reduces the amount of correction attributed to the surface layer at slope, ridge, and peak locations (Fig. 5).

The 3D corrections ΔTLk obtained from (5a) and (5b) are spatially interpolated using inverse-distance-squared (IDS) weighting in the horizontal, and IDS in potential temperature space in the vertical. The three-dimensional squared “distance” between an INCA grid point (i, j, m) and the kth station is given by
e6
Here, m denotes the vertical grid index, and the parameter c has the dimension of an inverse temperature gradient. Based on cross validation, its optimum values for both temperature and humidity were found to be close to 3 × 104 m K−1. A difference of 1 K in potential temperature is thus equivalent to a horizontal distance of about 30 km.
The three-dimensional temperature difference field is obtained from
e7
where n is the number of nearest stations used in the interpolation. [Note that “nearest” in this context means the smallest distance in the sense of (6).] Cross validation shows the smallest analysis errors for n between 6 and 10. The temperature difference field is then added to the 3D temperature forecast of the NWP model, giving
e8
The potential temperature weighting ensures that model errors as identified by station observations are not unphysically interpolated or extrapolated across stable layers.
The remaining differences, that is, those attributed to the surface layer, are determined from
e9
and interpolated horizontally, using a modified IDS weighting that takes into account the difference of inversion factors at grid point (i, j) and at the location of the kth station:
e10
where
e11
e12
Use of (12) ensures that corrections derived at a certain type of location (e.g., valley floor) are not interpolated to different types of location (e.g., slope). The final 2-m temperature analysis is obtained by adding the 2D surface-layer correction (10) to the 3D corrected model-level field (8) at the topography height z:
e13
The notation m(z) expresses the fact that TLINCA is evaluated (by linear interpolation) at the exact height of the INCA topography, so m may be a noninteger in this case. In principle, the method exactly reproduces observed temperatures at station locations. Due to the finite resolution of the grid, nonzero differences on the order of 0.1 K are found in practice. An analogous procedure is applied to the specific humidity field. An example of an INCA temperature analysis is given in Fig. 7.
Fig. 7.
Fig. 7.

Example of an INCA temperature analysis during a typical mixed foehn–inversion situation at 0800 UTC 21 Nov 2007. Cold-air pools are present in valleys and basins, while foehn-induced subsidence and mixing create high temperatures at elevations between 1500 and 2000 m.

Citation: Weather and Forecasting 26, 2; 10.1175/2010WAF2222451.1

An additional effect that is considered is the evaporative cooling due to precipitation, which becomes most significant in the case of isolated convective cells. The amount of cooling is parameterized thermodynamically as a function of precipitation amount and boundary layer humidity.

b. Wind

Like temperature and humidity, the INCA wind analysis is three-dimensional and based on an NWP field as a first guess. As in the case of temperature, a distinction is made between the model-level wind and the 10-m wind. To determine the differences between model-level wind and a 10-m wind observation at a mountain station, we must estimate the factor f10, which translates a model-level wind into a 10-m wind. Based on the ratio between the observed 10-m wind and the NWP-model-level wind, it was found that on average f10 ≈ 0.75 in lowland areas and in valleys. Higher above the valley floors, where the terrain is more exposed, the factor increases to 0.9. The exact value of this factor will depend on the NWP model used. After multiplying the observed wind by , differences of the u and υ components between the model and the observations are computed and interpolated analogous to the method used for temperature and humidity. However, the inverse distance squared interpolation of observation corrections does not produce a mass-consistent field, and the NWP wind forecast does not fit to the high-resolution INCA topography. Therefore, an iterative relaxation algorithm is applied to obtain a mass-consistent field satisfying
e14
and the kinematic boundary condition
e15
where n is the normal vector of the slope element and zH is the height of the INCA topography. In (14) only the vertical variation of density ρ = ρ(z) is considered. A brief discussion of different methods for obtaining mass-consistent wind fields is given by Wang et al. (2005). The algorithm used here is similar to the method introduced by Sherman (1978), but takes into account the reduced volume of grid boxes intersecting the terrain (Fig. 3) in the divergence computation. Wind vectors at grid points closest to station locations are kept at the observed values during the relaxation procedure. Such kinematic downscaling can simulate channeling and corner effects but cannot represent dynamical flow effects such as mountain waves, or vortices in the lee of steep topography (Wang et al. 2005), unless they are already present in the NWP field or in the observations.

c. Precipitation

The INCA precipitation analysis incorporates station data, radar data, and elevation effects. It is an attempt to combine the quantitative accuracy (compared to radar) of rain gauge measurements with the spatial accuracy provided by the radar field. Any combination method has to deal with the weaknesses of both types of observations as well, namely the limited representativeness and insufficient density of rain gauge stations and the quantitative uncertainty of precipitation estimation by radar. These problems are further enhanced by the Alpine topography. The individual steps of the analysis procedure are given below.

1) Interpolation of station data

The irregularly distributed 15-min rain gauge values are interpolated to the INCA grid (Fig. 8, top left) using IDS weighting:
e16
where
e17
is the squared distance between station k and grid point (i, j), and Pk is the observed precipitation at the kth station. The summation extends over the nearest eight stations. As in the case of temperature, a simple IDS interpolation was chosen because relatively little, if any, additional analysis skill can be gained by using a more sophisticated method such as kriging (Shen et al. 2001; Skok and Vrhovec 2006).
Fig. 8.
Fig. 8.

Example of a 15-min INCA precipitation analysis based on the combination of rain gauge and radar data (1930 UTC 19 Jun 2009): (top left) pure rain gauge interpolation, (top right) uncorrected max-CAPPI radar composite, (bottom left) corrected radar field, and (bottom right) final INCA precipitation analysis. Rain gauge locations are shown by black dots, and locations of radar stations are indicated by red triangles.

Citation: Weather and Forecasting 26, 2; 10.1175/2010WAF2222451.1

2) Climatological scaling of the radar data

The 2D radar data are aggregated to 15-min precipitation amounts and bilinearly interpolated onto the INCA grid. This initial, uncorrected radar field PRAD(i, j) is range dependent and contains systematic errors due to topographic shielding (Fig. 8, top right). Partial correction of these problems is achieved with a climatological scaling factor RFCM(i, j), which has been calculated for each month (M) of the year. It is the ratio between the multiyear, 3-monthly (from M − 1 to M + 1) accumulated precipitation obtained from station interpolation PSTA(i, j) and the corresponding accumulated radar precipitation PRAD(i, j):
e18
The resulting field is spatially smoothed to reduce sampling effects due to intense, localized precipitation events, which are not entirely cancelled out by the temporal accumulation in (18). The unsmoothed field is used to identify narrow weak sectors of the radar field. Multiplication of the radar field with RFC(i, j) (the index M is dropped hereafter) yields a climatologically adjusted radar field:
e19
One problem associated with this type of scaling is that regions where the radar field is very weak due to topographic radar beam shielding would yield arbitrarily high scaling factors, resulting in questionable precipitation values. This is avoided by imposing an upper limit on the scaling factor, which is currently set to the rather conservative value of 2. Higher limits were tested but created too many radar-related artifacts in the final analysis. Another problem is that more intense precipitation is generally seen better and thus less underestimated by radar. To prevent the overscaling of such cases, the scaling factor asymptotically decreases toward 1 for radar values higher than a certain scaling threshold PSC, currently set to 1 mm (15 min)−1:
e20
Thus, for a radar precipitation rate of PRAD = 10 mm (15 min)−1, the actual upper limit of the scaling is reduced from 2 to 1.2.

3) Rescaling of radar data using the latest observations

The climatologically scaled radar field is rescaled based on the comparison between station observations and the radar field at the station location. A maximum spatial shift of 4 km in either direction is permitted to take into account effects due to the finite settling time of hydrometeors, wind drift, and some uncertainty in the radar data localization. If a different radar pixel than the nearest one fits significantly better to the station observation, that value is used in subsequent calculations. A spatial shift vector is computed for every station location and interpolated to the grid using a distance-weighting algorithm. By applying this field of shift vectors to the radar precipitation field, it becomes slightly distorted to better match the rain gauge observations.

The actual rescaling then has the form
e21
which is strongly nonlinear, since both the weights wijk and the scaling factor RFAijk depend on the field itself. The weights have the form
e22
where rijk is given by (17). Compared to classical IDS weighting, (22) contains two additional terms. The additional term in the denominator increases the effective distance if the climatological scaling factor at a station and at the point in question are different. The coefficient has the value c = 10 km, which means that a difference in scaling factors of 1 is equivalent to a geometric distance of 10 km. This term is important, especially in mountain areas, where the RFC field can vary considerably over short distances due to topographic shielding. The term in the numerator reduces the weight if the radar precipitation value at a station is smaller than the one at the grid point in question. This avoids the scaling of higher-intensity radar values using scaling factors derived from much smaller ones. The scaling factor RFAijk in (21) is given by
e23
where the parameter RFA0 is a function of the climatological scaling at the grid point in question. In summary, the scaling defined by (21)(23) is a weighted average of the ratio between the rain gauge and radar precipitation at the nearest stations, where the weight decreases with increasing distance, with increasing difference in climatological scaling, and with decreasing precipitation at the station (relative to the precipitation at the grid point). The resulting field is shown in the bottom-left panel of Fig. 8. The rescaling based on rain gauge observations is also important for reducing errors in the areal precipitation distribution due to differences in radar reflectivity between rain and snow, since no explicit brightband correction is performed.

4) Final combination

The two precipitation fields PSTA(i, j) and are finally combined into a field PINCA(i, j), which gives a better estimate of the precipitation distribution than each individual field (Fig. 8, bottom right). The combination is obtained through a weighting relationship:
e24
where the weight w(i, j) decreases with increasing climatological scaling RFC(i, j), so that little weight is given in areas where radar returns are weak due to topographic shielding. The auxiliary field is created by interpolating onto the grid, analogous to the station observations, the scaled radar values at the station locations. At the station locations , so the station observations are reproduced there within the limits of the resolution. Between the stations, the weight of the radar information increases with the ability of the radar to capture the precipitation climatologically, that is, with decreasing RFC values. Figure 8 illustrates the large differences between the station interpolation and the unscaled radar field (top panels). The importance of the final combination (24) is that it reduces “edge” effects between areas covered by radar and those where the radar return is weak due to orographic shielding. In these areas the analysis reduces to station interpolation with elevation effects, as described below.

5) Elevation dependence

The inclusion of elevation effects turned out to be crucial for a realistic estimation of the spatial distribution of precipitation in Alpine areas, especially for hydrological applications. Based on feedback from hydrological simulations that used INCA precipitation analyses as an input, it was possible to constrain and optimize this parameterization.

In a first step, a “station topography” zSTA(i, j) is created. It is the topography represented by the reporting stations and is computed by IDS interpolation of INCA elevations at the station locations. Similarly, a “valley precipitation” field PVAL(i, j) is computed by an IDS interpolation identical to (16), but in which the summation extends only over those stations that are located not more than 300 m above the valley-floor surface. This field represents the reference precipitation at the valley-floor level. In the following we drop the gridpoint indices (i, j) for better legibility, where it is understood that each dependent variable is a gridded field.

Any parameterization of elevation effects for short durations must allow for zeros in the field. This requires it to be multiplicative rather than additive, and based on the relative precipitation gradient. We define the relative precipitation gradient between two elevations z1 and z2 as
e25
The precipitation increment due to the elevation effect in INCA is computed from
e26
where z is the local elevation as given by the INCA topography. The relative precipitation gradient GP is parameterized as a function of precipitation intensity as described in Haiden and Pistotnik (2009).
Finally, the increments due to radar field and elevation dependence are combined. Equation (24) can be rewritten as
e27
where
e28
In addition to the radar increment ΔPRAD, we now take into account the elevation increment ΔPELE as given by (26). To avoid a possible double counting of the elevation effects already present in the radar field, the combination is additive only if the increments have different signs:
e29a
If they have the same sign, then only the larger of the two increments is used:
e29b
Note that the elevation gradient is applied only to the rain-gauge-derived part of the precipitation field, and only to the extent at which is not at the same time seen by radar. This ensures that the precipitation from deep convective cells remains largely unaffected. The effects of the elevation parameterization can be seen in Fig. 8 by comparing the station interpolation with the final analysis in those Alpine areas where radar coverage is poor.

d. Precipitation type

Due to limited horizontal resolution, direct NWP model output of precipitation type may be of limited use in steep terrain and deep, narrow valleys where the model topography differs too much from the actual topography. This is an issue in flood forecasting, where the amount of precipitation stored as snow on the ground and not immediately contributing to runoff needs to be estimated, and in road weather forecasts as well. For such applications a distinction between rain and snow may be insufficient. In cases where the atmosphere is well mixed and the lapse rate close to moist adiabatic, the boundary between snowfall and rainfall will be relatively narrow. However, in more stable cases, or when the snowfall line has worked its way downward due to latent heat effects, there will be a broader height range with temperatures close to 0°C and associated conditions of snow–rain mix or wet snow.

In INCA the distinction between rain and snow is based on the vertical profile of the wet-bulb temperature Tw at each grid point, derived from the 3D temperature and humidity fields. Following Steinacker (1983) a snow–rain mix is assumed in the height range where 0°C ≤ Tw ≤ +2°C, marking the transition from snowfall into rain. Freezing rain is assumed to occur if rain falls into a surface layer with subzero air temperature, or if the ground temperature is below zero. In the latter case, however, the air temperature must not exceed a critical value set to +2°C. The analysis of ground surface temperature in INCA is based on observations of the +5-cm air temperature, −10-cm soil temperature, and 2-m air temperature. Outside the nowcasting range, the NWP forecast of ground surface temperature is used (corrected for the actual terrain height based on 2-m temperature).

e. Cloudiness

The INCA cloudiness analysis is actually an analysis of the insolation fraction SP as measured by surface stations, where MSG cloud-type data are used for spatial interpolation. The approach is similar to the INCA precipitation analysis in the sense that no NWP model output is used in the analysis, only in the forecast, and remote sensing data are calibrated using station observations. Also, a certain spatial shift (5 km) between a station location and a satellite pixel is allowed in order to take into account uncertainties in the timing and satellite imagery navigation as well as the slanted path of the sunbeam. Specific to cloudiness is the method of performing spatial interpolations of station observations separately for each set of stations that are located beneath the same cloud type. Thus, the relationship between cloud type and sunshine fraction on a given day does not get “smeared out” in areas where different cloud types are bordering each other. A weak smoothing is applied to the resulting field, consistent with the spatial shift between the station and satellite pixel described above. During nighttime, when no station observations of sunshine fraction are available, the SP field is constructed by combining cloud-type information with a scaling based on a monthly varying climatology instead of real-time observations. The relative mean absolute error of the cloudiness analysis, as obtained by cross validation, is about 18%. The cloudiness analysis provides input for the INCA global radiation analysis (Haiden et al. 2009).

4. Nowcasting methods

a. Lagrangian persistence

All INCA forecasts combine an observation-based extrapolation with an NWP model forecast. In the case of precipitation the extrapolation is based on Lagrangian persistence, derived from cross-correlating consecutive analyses. A relatively large (100 km × 100 km) correlation window is used to properly identify quasi-stationary orographic precipitation patterns. It was found that extrapolation based on smaller-scale motions leads to frequent unphysical advection of precipitation across the main Alpine crest. Unrealistically large motion vectors can also be due to spurious correlations. They are filtered by a comparison with the NWP wind field:
e30
where V is the motion vector derived by correlation analysis, VNWP is the 500- or 700-hPa wind (whichever is closer to V) in the NWP forecast, and Δ is a prescribed wind speed scale, which determines the amount of deviation permitted between V and VNWP. Operationally, the value Δ = 5 m s−1 is used. Equation (30) defines an elliptic area of permitted motion vectors (Fig. 9). Precipitation patterns are allowed to move in the general direction of the model wind at any speed that is lower than the model wind (such as quasi-stationary upslope rain), at a speed that is somewhat larger than the model wind, and slightly upstream.
Fig. 9.
Fig. 9.

NWP filtering of motion vectors according to (30). The boldface vector is the NWP model wind, and black arrows show examples of permitted motion vectors. Vectors outside the elliptic area are rejected. The dotted line shows the wind speed deviation parameter, Δ.

Citation: Weather and Forecasting 26, 2; 10.1175/2010WAF2222451.1

To obtain a continuous sequence of forecast fields, a transition from the extrapolation forecast to the NWP forecast is constructed through a prescribed weighting function that gives full weight to the extrapolation forecast during the first 2 h and decreases linearly to zero at 6 h. Attempts to improve upon the fixed weighting by making the time scale of the transition dependent on the magnitudes of NWP and nowcasting errors has as yet not shown any benefit.

Since the INCA precipitation analysis includes an elevation dependence, the extrapolation method described above cannot be applied directly to the final analysis but must be applied to intermediate fields that do not contain the elevation dependence. Thus, the “valley” precipitation is advected, and the elevation dependence is newly computed for each nowcast step. This ensures that the topographic patterns in the precipitation field remain stationary. As a result, precipitation patterns in the nowcast intensify as they move over higher terrain and weaken when they move over a valley. Note that this only applies to the station interpolation component of the combined field, not the radar component. Thus, convective cells are largely unaffected by this orographic effect in the extrapolation.

Nowcasts of cloudiness, or rather, sunshine fraction, are based on cloud motion vectors derived from consecutive visible (during daytime) and infrared (during nighttime) satellite images. During sunrise and sunset, a time-weighted combination of both motion vector fields is used. As in the case of precipitation, the extrapolation is performed for a forecast range of up to +6 h using a 15-min time step. The nowcasting of cloudiness includes a consistency check with the precipitation nowcast.

The NWP forecast of low, medium, and high cloudiness is converted to an insolation fraction forecast SP using the following linear relationship:
e31
with coefficients cl = 1, cm = 0.65, and ch = 0.35. The value 1 for cl was set based on the fact that low cloudiness efficiently shields the surface from direct solar radiation. The coefficients for medium and high cloudiness have been chosen for the closest correspondence between the NWP cloudiness forecast and the surface observations of SP and are, therefore, model dependent. The insolation fraction given by (31) is interpolated onto the INCA grid and blended with the sunshine fraction nowcast using the same weighting function as for precipitation. At night, the INCA cloudiness nowcast reduces to a climatologically scaled MSG cloud-type nowcast, since no VIS image and no surface observations are available.

b. NWP model trend

In the case of temperature and humidity, Lagrangian persistence explains only a small part of the total temporal variation, and variations due to the diurnal cycle become dominant. The temperature nowcast is based on the trend given by the NWP model and computed for each grid point from the recursive relationship
e32
where TINCA(t0) is the temperature at the analysis time. Thus, the INCA temperature nowcast is the latest analyzed temperature plus the temperature change predicted by the NWP model, multiplied by fT. This factor is parameterized as a function of the cloudiness forecast error of the NWP model. If the NWP model underestimates the cloudiness compared to the INCA cloudiness analysis and nowcast, it will tend to overpredict temperature changes, and vice versa. It is assumed that fT is linearly related to the cloudiness difference:
e33
where the coefficient cN was empirically found to be in the range 0.5–0.7, as determined from forecasting experiments in cases where the NWP model failed to predict persistent wintertime continental stratus. The cloud scheme in the ALADIN model has been improved with regard to the simulation of subinversion cloudiness (Kann et al. 2010), but the underforecasting of low stratus still represents a major error source in wintertime temperature forecasts. In such cases a correction such as (33) provides a useful reduction in the diurnal temperature variations. If the cloudiness forecast error is zero, fT = 1, and the temperature change in INCA is equal to that of the NWP model.

As in the case of precipitation and cloudiness, the temperature nowcast is blended into the NWP forecast. The time scale of the weighting function depends on static stability, varying from 3 h under well-mixed conditions to values of up to 12 h for pronounced inversion conditions. This dependency accounts for observed variations in the persistence of temperature forecast errors under different synoptic conditions. The humidity nowcast is analogous to temperature, without the cloudiness correction factor. Wind is treated similarly to temperature and humidity. The NWP trend is put on top of the analysis, and a fixed weighting function provides a transition into the NWP forecast.

5. Verification

There has been a renewed level of interest in the critical analysis of methods and measures used in meteorological forecast verification. Classical verification measures and scores are scrutinized with regard to statistical properties such as propriety and equitability (Mason 2008), and new spatial verification methods are being developed and compared (Gilleland et al. 2009). Here, we use one of the new object-oriented methods, Structure–Amplitude–Location (SAL; Wernli et al. 2008) to evaluate the INCA forecast skill and compare it with that of the ALADIN limited-area model, in addition to classical point verification. The analysis skill of INCA is quantified by cross validation based on single-station denial, looking at mean error (bias), mean absolute error (MAE), and root-mean-square error (RMSE).

a. Analysis verification

Evaluations have been performed for the analysis fields of temperature, precipitation, and wind. The verification is based on two months (July 2009 and January 2010), representing a summer and a winter month. Analyses of wind and temperature are at hourly time intervals, while the precipitation analysis has a temporal resolution of 15 min.

1) Temperature

For the cross validation of INCA temperature analyses, a total of 174 stations were used. The values of bias, MAE, and RMSE for different fields are given in Table 2 for July 2009 and January 2010. The bias in the temperature is small for both months over the averaging period. This is true not just for the average over all stations but also for individual stations, where the bias is typically less than 0.2°–0.3°C. MAE and RMSE are ∼20% higher in the winter month, due to the more frequent occurrence of stable stratifications and inversions. The distribution of individual analysis errors is shown by the scatterplot in Fig. 10. The majority of points lie within an interval of ±3°C from the diagonal, but differences of up to 5°–6°C do occur sometimes. Such large errors are associated with stable nighttime conditions and are found mainly in deep Alpine valleys, at locations where the distance to the nearest TAWES station is relatively large. Data points below 10°C in the diagram are mostly from mountaintop stations, where a stable boundary layer does not form. Consequently, maximum errors are smaller there.

Fig. 10.
Fig. 10.

Distribution of individual analysis–observation pairs in the INCA temperature cross validation for July 2009. For better legibility only data from every third analysis (0000, 0300, … , 2100 UTC) are shown.

Citation: Weather and Forecasting 26, 2; 10.1175/2010WAF2222451.1

Table 2.

Results of the INCA analysis cross validation for a summer and a winter month. Shown are the bias (mean error), MAE, and RMSE. For the wind components and for precipitation relative values are given as well.

Table 2.

2) Wind

The cross validation of the INCA wind has been performed for a limited region only. Due to the nonlocal effects of the divergence reduction algorithm, a full analysis had to be computed for each single-station denial of each analysis. The moderately hilly northern part of Austria (47.7°–49°N, 13°–17°E) was chosen as a verification area. Analyses are based on the 0000 UTC runs from ALADIN and all available station data. For the cross validation a sample of 36 stations was used. Values of bias, MAE, and RMSE have been calculated separately for the two wind components (Table 2). Since the magnitude of the analysis error strongly depends of the wind magnitude, relative measures with respect to the mean wind at each station are also given. They are computed as differences between the analyzed and observed winds at each station, divided by the observed period-mean wind speed at that station, and then averaged over all stations. Relative MAEs are on the order of 50%, and somewhat higher in July than in January. Like in the case of precipitation, as discussed below, there is a rather large uncertainty in the analysis of point values. The absolute MAE is about 1 m s−1.

3) Precipitation

In the case of precipitation, Table 2 shows values for the whole of Austria. With 257 stations used in the cross validation, the data density is higher than for the other parameters. While the bias equals zero again both for July and January, the RMSE reacts to the increased spatial variability associated with summertime convective precipitation. The relative measures show that the error with regard to point values for short-duration analyses (15 min) is rather large: around 50% in summer, and more than 100% in winter. This can be compared to results of Skok and Vrhovec (2006) who found point errors of up to 50% in cross validation for 24-h totals, relatively independent of the interpolation method. However, operational use of INCA precipitation analyses for hydrological simulations in small catchments of the order of 100 km2 indicates that areal averages are significantly more reliable than point values. Cross validations were also performed for two subdomains, one in the western part of Austria (Tyrol) and one in the east (Lower Austria). Differences between the mountainous west and the lowlands in the east are small in winter. In summer, the RMSE is significantly higher in the lowlands even though the MAE is virtually the same in both areas. This is because the RMSE is more sensitive to outliers, and there were an unusually high number of localized, heavy convective precipitation events in the eastern parts of Austria during July 2009. Figure 11 illustrates the correspondence between withheld observations and analyses for July 2009. As expected, the correlation is significantly weaker than in the case of temperature (Fig. 10), and errors of a factor 2 occur quite frequently in the analysis of 15-min precipitation amounts.

Fig. 11.
Fig. 11.

Distribution of individual analysis–observation pairs in the INCA 15-min precipitation cross validation for July 2009. For better legibility only data from every 12th analysis (0000, 0300, … , 2100 UTC) have been included in the plot. The line pattern is due to the finite resolution (0.1 mm) of the rain gauge data. Only values ≥0.1 mm are shown.

Citation: Weather and Forecasting 26, 2; 10.1175/2010WAF2222451.1

b. Nowcast verification

The SAL verification method (Wernli et al. 2008) has been applied to INCA precipitation forecasts for different subdomains in Austria for the same two months as in the cross validation. Lead times up to 12 h are considered. The location score shows little variability both for summer and winter. The average value over a mean (summer or winter) 12-h forecast period is approximately 0.3, with slightly lower values at the beginning of the period. The amplitude score also shows a rather similar pattern of behavior for summer and winter and for the different subdomains. There is an underestimation (about −0.5) of precipitation amounts in the first 6 h and an overestimation (about 0.5) in the second half of the forecast period due to the increasing influence of the NWP model for longer lead times. The structure score, which takes into account the shape and extension of precipitation fields, shows more distinct changes. For the first 4–5 h (i.e., the nowcasting period) it is close to 0, which means the structure is well represented. For longer lead times it increases to values between 1.0 and 1.5, indicating the structural loss in the INCA precipitation forecasts, when the kinematically extrapolated fields merge into NWP forecasts outside the nowcasting range. For lead times of up to 30 h, a more detailed evaluation is presented in Wittmann et al. (2010).

In terms of classical point verification against station observations, Fig. 12 shows the MAEs of the INCA and ALADIN temperature forecasts for lead times up to 12 h. The INCA MAE is significantly smaller than the ALADIN MAE over the first 3 h in summer and the first 6 h in winter. The residual benefit of INCA outside the nowcasting range is due to the downscaling procedure illustrated in Fig. 6. For precipitation, substantial improvement is usually found for the first 2–3 h.

Fig. 12.
Fig. 12.

INCA (dashed lines) and ALADIN (solid lines) MAEs of temperature, averaged over all stations, for July 2009 (gray) and January 2010 (black).

Citation: Weather and Forecasting 26, 2; 10.1175/2010WAF2222451.1

6. Summary and conclusions

The central European multivariable analysis and nowcasting system INCA adds value to NWP forecasts by providing analyses, nowcasts, and downscaled forecasts. Analysis methods for temperature and precipitation include topographic effects, which makes the system applicable to mountainous terrain. For temperature and humidity it takes into account the ability of the local topography to support a pronounced surface layer. For precipitation, the local elevation dependence is included as part of a nonlinear combination of rain gauge and radar data. Nowcasting of precipitation and cloudiness is based on the classical advection method with motion vectors derived from the cross correlation of previous analyses. Nowcasting of temperature, humidity, and wind is Eulerian, and based on modified trends of the NWP model.

Based on cross validation of INCA analyses, verification of INCA nowcasts, and qualitative assessments during day-to-day applications of the system, the following main conclusions can be drawn.

  • Temperature is analyzed with an average accuracy of 1°–1.5°C, with the smallest errors occurring in lowland areas and at exposed mountain stations. In Alpine valleys the mean absolute error is larger (1.5°–2.5°C), mostly due to the lack of precise information about inversion heights.

  • A combination of radar and rain gauge information and the inclusion of a parameterization of elevation effects is essential for providing useful precipitation analyses in the eastern Alpine region. However, the relationship between scaled radar values and rain gauge observations shows rather high variability, and the mean relative analysis error for 15-min precipitation amounts is on the order of 50%–100%.

  • Both wind and precipitation analyses suffer from representativeness problems, due to substantial subgrid-scale variations in these fields, in spite of the 1-km resolution of the analysis system.

  • Comparison of INCA and NWP forecast skill shows that substantial improvements due to nowcasting are achieved in the first 2–3 h for precipitation, and in the first 6 h for temperature.

  • Outside the nowcasting range, INCA improves the NWP temperature forecast through downscaling by 5%–10%.

The development of new, and the modification of existing, analysis and nowcasting methods by accounting for topographic effects allows nowcasting methodology to be applied to Alpine terrain. However, many processes relevant for nowcasting, such as slope and valley winds, or the vertical temperature profile in inversion cases, are not adequately resolved even by the rather dense observational network in the eastern Alps. This limits our current analysis and nowcasting capability in such areas. Further improvements are unlikely to come from denser station networks but rather from more comprehensive use of satellite data, and from high-resolution NWP. The European Union project INCA-CE—A Central European Initiative in Nowcasting Applications, which began in 2010, will provide a framework for such developments. Emphasis will be on improved wind downscaling based on high-resolution NWP and on enhanced use of satellite data in the nowcasting of deep convection. Situation-dependent blending of the nowcast into the NWP forecast will be another priority in future INCA development.

Acknowledgments

We are grateful to three anonymous reviewers for constructive comments that helped to improve this paper. We want to thank Klaus Stadlbacher, Alexander Beck, and Martin Auer for their work on the operational aspects of INCA. We would also like to thank the hydrological authorities of Lower Austria and Salzburg for funding projects that led to improvements in the INCA precipitation and temperature nowcast.

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