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P. Bauer
,
L. Schanz
, and
L. Roberti

Abstract

This paper presents a simple approach to adjust microwave brightness temperature distributions obtained from slant-path measurements for projection effects. Horizontal displacement in the direction of sight is caused by signal contributions from other than near-surface layers that are projected to the footpoint of observation. In particular at frequencies sensitive to ice particle scattering the horizontal projection effect can amount to values as big as the vertical cloud extent. Based on cloud model–generating, three-dimensional hydrometeor distributions at subsequent model time steps and a modified one-dimensional radiative transfer model, the high correlation of effective radiance contribution altitudes and brightness temperatures at 37.0 and 85.5 GHz is demonstrated. For these altitudes, described by the centers of gravity of the spectral weighting functions, regression equations are derived with standard errors below 0.61 km at 85.5 GHz and 0.22 km at 37.0 GHz for both the Special Sensor Microwave/Imager (SSM/I) and Tropical Rainfall Measurement Mission Microwave Imager. Once the centers of gravity are retrieved a simple geometry correction can be applied to the measurements.

Application to model cloud fields at various time steps and different oberservation geometries shows a significantly improved correspondence of brightness temperature and hydrometeor distributions. This method is also applied to SSM/I observations during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment in the equatorial Pacific. Considerable improvements of single-channel rain retrievals based on 85.5-GHz measurements compared to shipborne radar data were achieved, which suggests that a major uncertainty of so-called scattering algorithms can be explained by geometry effects that can be easily corrected. Multichannel algorithms, however, require a more elaborate integration scheme to allow for both frequency and scene-dependent adjustments.

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P. Bauer
,
L. Schanz
,
R. Bennartz
, and
P. Schlüssel

Abstract

The status of current rainfall-retrieval techniques by satellite radiometry has been evaluated by recent international algorithm intercomparison projects. As a general result, passive microwave techniques perform superiorly for instantaneous applications over oceans, while infrared or combined infrared–microwave techniques show improved monthly rainfall accumulations, mainly due to the high temporal sampling by geosynchronous observations. Merging microwave, visible, and infrared imagery data available on the same satellite such as the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the Visible Infrared Scanner (VIRS) provides further potential for the improvement of instantaneous retrievals. A case study is shown that demonstrates the stepwise degradation of information contained in the microwave signals when three-dimensional cloud effects and realistic antenna patterns are simulated for a convective cloud obtained from Doppler polarization radar soundings. Simultaneous visible and infrared data may contribute mainly to better rain-regime classification, in particular when sophisticated cloud identification techniques and cloud parameter retrievals are incorporated. Although the beam-filling problem is not solved by the TMI–VIRS combination alone, some other progress, for example, concerning better coastline treatment, is shown.

With respect to monthly products and the climatologically important observation of diurnal rainfall variations, the TRMM sensor combination will provide a calibration standard to be applied to geosynchronous sensors.

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A. Benedetti
,
P. Lopez
,
E. Moreau
,
P. Bauer
, and
V. Venugopal

Abstract

A validation of passive microwave–adjusted rainfall analyses of tropical cyclones using spaceborne radar data is presented. This effort is part of the one-dimensional plus four-dimensional variational (1D+4D-Var) rain assimilation project that is being carried out at the European Centre for Medium-Range Weather Forecasts (ECMWF). Brightness temperatures or surface rain rates from the Tropical Rainfall Measuring Mission (TRMM) satellite are processed through a 1D-Var retrieval to derive values of total column water vapor that can be ingested into the operational ECMWF 4D-Var. As an indirect validation, the precipitation fields produced at the end of the 1D-Var minimization process are converted into equivalent radar reflectivity at the frequency of the TRMM precipitation radar (13.8 GHz) and are compared with the observations averaged at model resolution. The averaging process is validated using a sophisticated downscaling/upscaling approach that is based on wavelet decomposition. The precipitation radar measurements are ideal for this validation exercise, being approximately collocated with but completely independent of the TRMM Microwave Imager (TMI) radiometer measurements. Qualitative and statistical comparisons between radar observations and retrievals from the TMI-derived surface rain rates and from TMI radiances are made using 17 well-documented tropical cyclone occurrences between January and April of 2003. Several statistical measures, such as bias, root-mean-square error, and Heidke skill score, are introduced to assess the 1D-Var skill as well as the model background skill in producing a realistic rain distribution. Results show a good degree of skill in the retrievals, especially near the surface and for medium–heavy rain. The model background produces precipitation in the domain that is sometimes in excess with respect to the observations, and it often shows an error in the location of precipitation maxima. Differences between the two 1D-Var approaches are not large enough to make final conclusions regarding the advantages of one method over the other. Both methods are capable of redistributing the rain patterns according to the observations. It appears, however, that the brightness temperature approach is in general more effective in increasing precipitation amounts at moderate-to-high rainfall rates.

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P. Bauer
,
J. P. V. Poiares Baptista
, and
M. de Iulis

Abstract

A study was carried out in order to estimate the effect of melting particles on simulated brightness temperatures at microwave frequencies between 10.7 and 85.5 GHz for precipitation over the ocean. The meteorological model framework is based on the assumption that the strongest radiometric effect is due to the drastically increased permittivity of melting particles driven by the volume fraction of liquid water. Thus, effects caused by particle aggregation and breakup are neglected.

Different approaches for calculating the effective permittivity of mixed particles are compared. The resulting extinction coefficients, single scattering albedos, and asymmetry parameters indicate a maximum effect when the particle is composed of a water matrix with air–ice inclusions. In particular the extinction coefficient may vary by more than two orders of magnitude right below the freezing level dependent on frequency and the applied mixing formula. In the melting region also the strongest dependence of the optical properties on the droplet spectrum is observed. Extreme local differences of 100% between the particle optical properties employing either a Marshall–Palmer or gamma-type drop size distribution occur.

When radiative transfer calculations are carried out, average deviations of 20–30 K at low frequencies (10.7 and 19.35 GHz) are observed, mainly due to the strong dependence of the extinction coefficient on the implemented melting process. However, this effect is driven by the applied mixing formula rather than the drop size distribution; that is, for particles composed of a water matrix and air–ice inclusions independently of melting stage the emission excess seems to be overexpressed.

The systematic effect of including the melting process in radiative transfer calculations for the development of surface rain retrievals was also investigated. Over 550 model atmospheres were used to estimate the relative deviation of surface rain-rate estimates using a set of operational rain retrieval algorithms. Neglecting the melting effect may lead to severe overestimations of surface rain rates by up to 100% in stratiform conditions. However, if the melting layer is either weakly expressed or nonuniformly distributed in space, the relative overestimation is much lower. If the effective permittivity of melting particles is calculated using the weighted mixing approach of Meneghini and Liao, considerably less effect of melting particles on passive microwave emission is observed.

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F. Harnisch
,
S. B. Healy
,
P. Bauer
, and
S. J. English

Abstract

An ensemble of data assimilations (EDA) approach is used to estimate how the impact of Global Navigation Satellite System (GNSS) radio occultation (RO) measurements scales as a function of observation number in the ECMWF numerical weather prediction system. The EDA provides an estimate of the theoretical analysis and short-range forecast error statistics, based on the ensemble “spread,” which is the standard deviation of the ensemble members about the ensemble mean. This study is based on computing how the ensemble spread of various parameters changes as a function of the number of simulated GNSS RO observations. The impact from 2000 up to 128 000 globally distributed simulated GNSS RO profiles per day is investigated. It is shown that 2000 simulated GNSS RO measurements have an impact similar to real measurements in the EDA and that the EDA-based impact of real data can be related to the impact in observing system experiments. The dependence of the ensemble statistics on observation error statistics assumed when assimilating the data, rather than the actual observation errors, is emphasized. There is no evidence of “saturation” of forecast impact even with 128 000 GNSS RO profiles per day. However, this result is a well-known consequence of always improving the theoretical analysis and short-range forecast error statistics when adding new observations that are assumed to have uncorrelated observation errors. In general, it is found that 16 000 GNSS RO profiles per day have around half the impact of 128 000 profiles, based on the reduction of ensemble spread values where the GNSS RO measurements have the largest impact.

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F. Chevallier
,
P. Bauer
,
G. Kelly
,
C. Jakob
, and
T. McNally

Abstract

Radiation observations are a key element in the evaluation of the 40-yr reanalysis at the European Centre for Medium-Range Weather Forecasts. This paper uses the High-Resolution Infrared Radiation Sounder/2 (HIRS/2) and Microwave Sounding Unit (MSU) observations on board the National Oceanic and Atmospheric Administration satellites, to assess the characteristics of the cloud fields produced by the forecasting system over midlatitude and tropical oceans. Infrared and microwave radiation have different sensitivities to clouds and are therefore complementary. Observed and model-generated radiances, as well as HIRS/2-derived cloud parameters, are compared.

The model clouds are shown to be well distributed, with realistic seasonal cycles. However, deficiencies are identified and discussed: the cloud radiative impact may be too low in the midlatitudes, the frequency of occurrence of high clouds is overestimated in the intertropical convergence zone, and the stratocumulus off the west coast of the continents is underestimated. The methods described here provide a framework for assessing the impact of forthcoming improvements to the cloud scheme.

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G. A. Kelly
,
P. Bauer
,
A. J. Geer
,
P. Lopez
, and
J-N. Thépaut

Abstract

This paper presents the results from the Observing System Experiments (OSEs) with the current ECMWF data assimilation and modeling system for quantifying the impact on both analysis and forecast quality of Special Sensor Microwave Imager (SSM/I) observations sensitive to moisture and clouds as well as precipitation. SSM/I radiances have been assimilated operationally in clear-sky areas for 8 yr and in cloud- and rain-affected areas since June 2005. This paper examines experiments set up such that clear-sky and rain-affected observations were either added to a baseline with a restricted observing system configuration or withdrawn from the full system. The experiment duration was 10 weeks of which the first 14 days were excluded from the evaluation to allow the system to lose the memory of the initial conditions at day −1.

It is shown that both clear-sky and rain-affected observations account for the bulk correction of moisture in the ECMWF analysis. SSM/I data adds 1 day of forecast skill over the first 48 h when evaluated in addition to a baseline-observing system. In the tropics, the rain-affected data contributes more skill to the moisture forecast than the clear-sky data at 700 hPa and above. In the Northern and Southern Hemispheres, the effect is generally weaker and slightly in favor of clear-sky observations. A similar performance can be seen with respect to the wind vector forecast skill, which reflects the connection between the analysis of moisture and dynamics.

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Peter Bauer
,
A. Khain
,
A. Pokrovsky
,
R. Meneghini
,
C. Kummerow
,
F. Marzano
, and
J. P. V. Poiares Baptista

Abstract

The simulation of explicit particle spectra during cloud evolution by a two-dimensional spectral cloud model was used to investigate the response of microwave radiative transfer to particle spectra development with special focus on the radiative effects of melting particles below the freezing level. For this purpose, 1) a particle-melting model was implemented with increased vertical resolution; 2) several models of the dielectric permittivity for melting particles were compared; 3) the dependence on size–density distributions was evaluated; and 4) the influence on the results by the replacement of explicit by parameterized particle spectra was tested.

Radiative transfer simulations over ocean background at frequencies between 10.7 and 85.5 GHz showed a considerable increase in brightness temperatures (T B ) once melting particles were included. The amounts were strongly dependent on the implemented permittivity model, the number concentrations of large frozen particles right above the freezing level, and the local cloud conditions. Assuming a random mixture of air, ice, and meltwater in the particle, T B s increased by up to 30 K (at 37.0 GHz) in the stratiform cloud portion for nadir view. If the meltwater was taken to reside at the particle boundaries, unrealistic T B changes were produced at all frequencies. This led to the conclusion that for large tenuous snowflakes the random-mixture model seems most appropriate, while for small and dense particles a nonuniform water distribution may be realistic. The net melting effect on simulated T B s, however, depended strongly on attenuation by supercooled liquid water above the freezing level, which generally suppressed the signal at 85.5 GHz. Over land background, changes in T B due to melting particles remained below 8 K, which would be difficult to identify compared to variations in surface emission and cloud profile heterogeneity.

Replacement of the explicit particle spectra for rain, snow, and graupel by parameterized spectra (here, in exponential form with a fixed intercept) produced reductions of the melting signature by up to 40% over ocean. It was found that exponential size distribution formulas tended to underestimate number concentrations of large particles and overestimated those of small particles at those cloud levels where sufficient particle sedimentation leads to collection, aggregation, and evaporation, respectively. Consequently, the strongest differences between explicit and parameterized spectra occurred right above the freezing level for snow and graupel, and close to the surface for rain. Radiometrically, this resulted in an underestimation of scattering above the freezing level and an underestimation of emission by melting particles below the freezing level as well as by rain toward the surface. In the stratiform region, the net effect was a reduction of the melting signature; however, T B ’s were still up to 15 K higher than from the no-melting case for the random-mixture permittivity model.

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C. Kidd
,
P. Bauer
,
J. Turk
,
G. J. Huffman
,
R. Joyce
,
K.-L. Hsu
, and
D. Braithwaite

Abstract

Satellite-derived high-resolution precipitation products (HRPP) have been developed to address the needs of the user community and are now available with 0.25° × 0.25° (or less) subdaily resolutions. This paper evaluates a number of commonly available satellite-derived HRPPs covering northwest Europe over a 6-yr period. Precipitation products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center (CPC) morphing (CMORPH) technique, the CPC merged microwave technique, the Naval Research Laboratory (NRL) blended technique, and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) technique. In addition, the Geosynchronous Operational Environmental Satellite (GOES) precipitation index (GPI) and the European Centre for Medium-Range Weather Forecasting (ECMWF) operational forecast model products are included for comparison. Surface reference data from the European radar network is used as ground truth, supported by the Global Precipitation Climatology Centre (GPCC) precipitation gauge analysis and gauge data over the United Kingdom. Measures of correlation, bias ratio, probability of detection, and false alarm ratio are used to evaluate the products. Results show that satellite products generally exhibit a seasonal cycle in correlation, bias ratio, probability of detection, and false alarm ratio, with poorer statistics during the winter. The ECMWF model also shows a seasonal cycle in the correlation, although the results are poorer during the summer, while the bias ratio, probability of detection, and false alarm ratio are consistent through all seasons. Importantly, all the satellite HRPPs underestimate precipitation over northwest Europe in all seasons.

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E. A. Smith
,
J. E. Lamm
,
R. Adler
,
J. Alishouse
,
K. Aonashi
,
E. Barrett
,
P. Bauer
,
W. Berg
,
A. Chang
,
R. Ferraro
,
J. Ferriday
,
S. Goodman
,
N. Grody
,
C. Kidd
,
D. Kniveton
,
C. Kummerow
,
G. Liu
,
F. Marzano
,
A. Mugnai
,
W. Olson
,
G. Petty
,
A. Shibata
,
R. Spencer
,
F. Wentz
,
T. Wilheit
, and
E. Zipser

Abstract

The second WetNet Precipitation Intercomparison Project (PIP-2) evaluates the performance of 20 satellite precipitation retrieval algorithms, implemented for application with Special Sensor Microwave/Imager (SSM/I) passive microwave (PMW) measurements and run for a set of rainfall case studies at full resolution–instantaneous space–timescales. The cases are drawn from over the globe during all seasons, for a period of 7 yr, over a 60°N–17°S latitude range. Ground-based data were used for the intercomparisons, principally based on radar measurements but also including rain gauge measurements. The goals of PIP-2 are 1) to improve performance and accuracy of different SSM/I algorithms at full resolution–instantaneous scales by seeking a better understanding of the relationship between microphysical signatures in the PMW measurements and physical laws employed in the algorithms; 2) to evaluate the pros and cons of individual algorithms and their subsystems in order to seek optimal “front-end” combined algorithms; and 3) to demonstrate that PMW algorithms generate acceptable instantaneous rain estimates.

It is found that the bias uncertainty of many current PMW algorithms is on the order of ±30%. This level is below that of the radar and rain gauge data specially collected for the study, so that it is not possible to objectively select a best algorithm based on the ground data validation approach. By decomposing the intercomparisons into effects due to rain detection (screening) and effects due to brightness temperature–rain rate conversion, differences among the algorithms are partitioned by rain area and rain intensity. For ocean, the screening differences mainly affect the light rain rates, which do not contribute significantly to area-averaged rain rates. The major sources of differences in mean rain rates between individual algorithms stem from differences in how intense rain rates are calculated and the maximum rain rate allowed by a given algorithm. The general method of solution is not necessarily the determining factor in creating systematic rain-rate differences among groups of algorithms, as we find that the severity of the screen is the dominant factor in producing systematic group differences among land algorithms, while the input channel selection is the dominant factor in producing systematic group differences among ocean algorithms. The significance of these issues are examined through what is called “fan map” analysis.

The paper concludes with a discussion on the role of intercomparison projects in seeking improvements to algorithms, and a suggestion on why moving beyond the “ground truth” validation approach by use of a calibration-quality forward model would be a step forward in seeking objective evaluation of individual algorithm performance and optimal algorithm design.

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