Abstract

A method is presented for deriving physically consistent profiles of temperature, humidity, and cloud liquid water content. This approach combines a ground-based multichannel microwave radiometer, a cloud radar, a lidar-ceilometer, the nearest operational radiosonde measurement, and ground-level measurements of standard meteorological properties with statistics derived from results of a microphysical cloud model. All measurements are integrated within the framework of optimal estimation to guarantee a retrieved profile with maximum information content. The developed integrated profiling technique (IPT) is applied to synthetic cloud model output as a test of accuracy. It is shown that the liquid water content profiles obtained with the IPT are significantly more accurate than common methods that use the microwave-derived liquid water path to scale the radar reflectivity profile. The IPT is also applied to 2 months of the European Cloud Liquid Water Network (CLIWA-NET) Baltic Sea Experiment (BALTEX) BRIDGE main experiment (BBC) campaign data, considering liquid-phase, nonprecipitating clouds only. Error analysis indicates root-mean-square uncertainties of less than 1 K in temperature and less than 1 g m⁻³ in humidity, where the relative error in liquid water content ranges from 15% to 25%. A comparison of the vertically integrated humidity profile from the IPT with the nearest operational radiosonde shows an acceptable bias error of 0.13 kg m⁻² when the Rosenkranz gas absorption model is used. However, if the Liebe gas absorption model is used, this systematic error increases to −1.24 kg m⁻², showing that the IPT humidity retrieval is significantly dependent on the chosen gas absorption model.

Abstract

Nimbus-7 SMMR data and ship observations are combined to compute the latent heat flux using the bulk aerodynamic method. Sea surface temperature (SST) and the surface humidity are determined with the microwave data. The surface wind field is derived from an analysis of ship observations of wind speed and surface pressure by means of a boundary-layer model by Bumke and Hasse. The microwave-derived SSTs are calibrated against those calculated from Advanced Very High-Resolution Radiometer (AVHRR) data. To get reliable results in the northern parts of the North Atlantic, only ascending (daytime) orbits of Nimbus-7 were used. Daytime data show a larger bias due to solar heating of the instrument but lack the complicating effects of differential cooling when the satellite enters the earth's shadow at the beginning of the descending orbits.

The evaporation fields are derived over the North Atlantic for individual overpasses of the satellite during July 1983, with a spatial resolution of 1° × 1°. High temporal and spatial gradients are observed, which are consistent with the prevailing synoptic situations. In the area south of Greenland and east of Canada, where the Labrador Current is located, latent heat flux (LE) is negative even in the monthly mean. The reliability of the negative values is demonstrated by a case study. They coincide well with ship observations of fog events.

The flux of latent heat can be determined with an acceptable accuracy of 25–40 W m⁻² for individual values if the bias of the SMMR data can be reliably removed.

Abstract

In this study, reanalysis data and a long-term simulation with the regional climate model WRF (1982–2017; 10 km resolution) is used to analyze synoptic and regional processes associated with rainfall events in the Atacama Desert. Five composites, each with 10 WRF-simulated rainfall events, are studied. They are selected based on a clustering and comprise the top winter events in South Atacama (23°–26°S), Southeast Atacama, and North Atacama (18°–23°S), and the top summer events in North Atacama and Northeast Atacama. Winter rainfall events in South Atacama are mostly associated with strong low pressure systems over the southeast Pacific and atmospheric rivers at their foreside, while cutoff lows occurring anomalously far north facilitate strong rainfall in North Atacama. Accordingly, tropical continental areas and the remote tropical and subtropical Pacific are identified as primary moisture sources, and moisture transport toward the Atacama Desert mainly takes place in the free troposphere (above 800 hPa). Strong summer rainfall events in North Atacama and Northeast Atacama are associated with a southward displaced Bolivian high. During rainfall events in North Atacama the high is shifted westward when compared to the Northeast Atacama events. Consequently, northern Chile is located at the northern periphery of the Bolivian high and the resulting strong easterlies may push strong convective systems from the Altiplano, toward the Atacama coast. Detailed analyses of individual rainfall events reveal that the most important synoptic patterns associated with rainfall not only control the synoptic-scale moisture transport into the Atacama Desert, but also decisively influence the regional atmospheric circulation.

Abstract

In many hyperarid ecosystems, such as the Atacama Desert, fog is the most important freshwater source. To study biological and geological processes in such water-limited regions, knowledge about the spatiotemporal distribution and variability of fog presence is necessary. In this study, in situ measurements provided by a network of climate stations equipped, inter alia, with leaf wetness sensors are utilized to create a reference fog dataset that enables the validation of satellite-based fog retrieval methods. Further, a new satellite-based fog-detection approach is introduced that uses brightness temperatures measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) as input for a neural network. Such a machine learning technique can exploit all spectral information of the satellite data and represent potential nonlinear relationships. Relative to a second fog-detection approach based on MODIS cloud-top height retrievals, the neural network reaches a higher detection skill (Heidke skill score of 0.56 as compared with 0.49). A suitable representation of temporal variability on subseasonal time scales is provided with correlations mostly greater than 0.7 between fog occurrence time series derived from the neural network and the reference data for individual climate stations, respectively. Furthermore, a suitable spatial representativity of the neural-network approach to expand the application to the whole region is indicated. Three-year averages of fog frequencies reveal similar spatial patterns for the austral winter season for both approaches. However, differences are found for the summer and potential reasons are discussed.

Abstract

Two power-law relations linking equivalent radar reflectivity factor Z _e and snowfall rate S are derived for a K-band Micro Rain Radar (MRR) and for a W-band cloud radar. For the development of these Z _e –S relationships, a dataset of calculated and measured variables is used. Surface-based video-disdrometer measurements were collected during snowfall events over five winters at the high-latitude site in Hyytiälä, Finland. The data from 2014 to 2018 include particle size distributions (PSD) and their fall velocities, from which snowflake masses were derived. The K- and W-band Z _e values are computed using these surface-based observations and snowflake scattering properties as provided by T-matrix and single-particle scattering tables, respectively. The uncertainty analysis shows that the K-band snowfall-rate estimation is significantly improved by including the intercept parameter N ₀ of the PSD calculated from concurrent disdrometer measurements. If N ₀ is used to adjust the prefactor of the Z _e –S relationship, the RMSE of the snowfall-rate estimate can be reduced from 0.37 to around 0.11 mm h⁻¹. For W-band radar, a Z _e –S relationship with constant parameters for all available snow events shows a similar uncertainty when compared with the method that includes the PSD intercept parameter. To demonstrate the performance of the proposed Z _e –S relationships, they are applied to measurements of the MRR and the W-band microwave radar for Arctic clouds at the Arctic research base operated by the German Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) and the French Polar Institute Paul Emile Victor (IPEV) (AWIPEV) in Ny-Ålesund, Svalbard, Norway. The resulting snowfall-rate estimates show good agreement with in situ snowfall observations while other Z _e –S relationships from literature reveal larger differences.

Abstract

Low-level-jet (LLJ) periods are investigated by exploiting a long-term record of ground-based remote sensing Doppler wind lidar measurements supported by tower observations and surface flux measurements at the Jülich Observatory for Cloud Evolution (JOYCE), a midlatitude site in western Germany. LLJs were found 13% of the time during continuous observations over more than 4 yr. The climatological behavior of the LLJs shows a prevailing nighttime appearance of the jets, with a median height of 375 m and a median wind speed of 8.8 m s⁻¹ at the jet nose. Significant turbulence below the jet nose only occurs for high bulk wind shear, which is an important parameter for describing the turbulent characteristics of the jets. The numerous LLJs (16% of all jets) in the range of wind-turbine rotor heights below 200 m demonstrate the importance of LLJs and the associated intermittent turbulence for wind-energy applications. Also, a decrease in surface fluxes and an accumulation of carbon dioxide are observed if LLJs are present. A comprehensive analysis of an LLJ case shows the influence of the surrounding topography, dominated by an open pit mine and a 200-m-high hill, on the wind observed at JOYCE. High-resolution large-eddy simulations that complement the observations show that the spatial distribution of the wind field exhibits variations connected with the orographic flow depending on the wind direction, causing high variability in the long-term measurements of the vertical velocity.

Abstract

Based on a comparison of ground-based radiometer measurements with microwave radiative transfer calculations, it is shown that raindrops with an oblate shape and a preferred horizontal orientation have a significant effect on microwave polarization signals when compared with spherical particle shape. Measurements with a dual-polarized 19-GHz radiometer reveal a polarization difference of as much as −18 K in the downwelling microwave radiation at 30° elevation angle. Averaging all rain observations within 19 months leads to a signal of −6 K. Model calculations covering roughly the same range of weather conditions as that inferred from the meteorological data recorded with the radiometer measurements were carried out with spherical raindrop shape and an oblate particle shape with a fixed horizontal alignment. From the model results, positive polarization difference is expected for spherical particles. This signal was never observed in the recorded data. For oblate drops, the averaged model results lead to a polarization difference of −8 K, which is in reasonable agreement with the long-term averaged observations. Case studies that compare isolated rain events usually lead to a better match of model and observations. However, there are some major discrepancies in some cases. Possible reasons for the remaining differences are the short-term variations in the cloud microphysics for which the model does not correctly account, such as variations in the melting layer, drop oscillations, or variations in the drop size distribution or angular distribution of the drop alignment. Three-dimensional effects are also important when observing small-scale heavy precipitation. Despite remaining small uncertainties, the comparison presents strong evidence that the oblate raindrop shape, with fixed horizontal alignment, is by far the better choice for accurate radiative transfer calculations than is the spherical shape. The omission of this shape effect can cause significant errors when developing remote sensing algorithms based on model results.

Abstract

Real midlatitude meteorological cases are simulated over western Europe with the cloud mesoscale model Méso-NH, and the outputs are used to calculate brightness temperatures at microwave frequencies with the Atmospheric Transmission at Microwave (ATM) radiative transfer model. Satellite-observed brightness temperatures (TBs) from the Advanced Microwave Scanning Unit B (AMSU-B) and the Special Sensor Microwave Imager (SSM/I) are compared to the simulated ones. In this paper, one specific situation is examined in detail. The infrared responses have also been calculated and compared to the Meteosat coincident observations. Overall agreement is obtained between the simulated and the observed brightness temperatures in the microwave and in the infrared. The large-scale dynamical structure of the cloud system is well captured by Méso-NH. However, in regions with large quantities of frozen hydrometeors, the comparison shows that the simulated microwave TBs are higher than the measured ones in the window channels at high frequencies, indicating that the calculation does not predict enough scattering. The factors responsible for the scattering (frozen particle distribution, calculation of particle dielectric properties, and nonsphericity of the particles) are analyzed. To assess the quality of the cloud and precipitation simulations by Méso-NH, the microphysical fields predicted by the German Lokal-Modell are also considered. Results show that in these midlatitude situations, the treatment of the snow category has a high impact on the simulated brightness temperatures. The snow scattering parameters are tuned to match the discrete dipole approximation calculations and to obtain a good agreement between simulations and observations even in the areas with significant frozen particles. Analysis of the other meteorological simulations confirms these results. Comparing simulations and observations in the microwave provides a powerful evaluation of resolved clouds in mesoscale models, especially the precipitating ice phase.

Abstract

The simulations of five midlatitude precipitating events by the nonhydrostatic mesoscale model Méso-NH are analyzed. These cases cover contrasted precipitation situations from 30° to 60°N, which are typical of midlatitudes. They include a frontal case with light precipitation over the Rhine River area (10 February 2000), a long-lasting precipitation event at Hoek van Holland, Netherlands (19 September 2001), a moderate rain case over the Elbe (12 August 2002), an intense rain case over Algiers (10 November 2001), and the “millennium storm” in the United Kingdom (30 October 2000). The physically consistent hydrometeor and thermodynamic outputs are used to generate a database for cloud and precipitation retrievals. The hydrometeor vertical profiles that were generated vary mostly with the 0°C isotherm, located between 1 and 3 km in height depending on the case. The characteristics of this midlatitude database are complementary to the GPROF database, which mostly concentrates on tropical situations. The realism of the simulations is evaluated against satellite observations by comparing synthetic brightness temperatures (BTs) with Advanced Microwave Sounding Unit (AMSU), Special Sensor Microwave Imager (SSM/I), and Meteosat observations. The good reproduction of the BT distributions by the model is exploited by calculating categorical scores for verification purposes. The comparison with 3-hourly Meteosat observations demonstrates the ability of the model to forecast the time evolution of the cloud cover, the latter being better predicted for the stratiform cases than for others. The comparison with AMSU-B measurements shows the skill of the model to predict rainfall at the correct location.