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Rolf Fuhrhop and Clemens Simmer

Abstract

The incidence angles of the SSM/I radiometers on the DMSP satellites vary from satellite to satellite and exhibit variations of up to 1.5° during one orbit. The effects of these variations on the measured brightness temperatures are investigated on the basis of simulated and measured data for oceanic arm. A deviation of 1° from the nominal incidence angle of 53.0° causes brightness temperature changes of up to 2 K depending on surface and atmospheric conditions. Errors of retrieved geophysical parameters on the order of 5%–10% result when the incidence angle variation is not taken into account. This is a common property of most published statistical algorithms. For total precipitable water and cloud liquid water content the error increases with increasing parameter value. For wind speed the error is largest for low wind speed and decreases with increasing wind speed. Due to the slowly varying latitudinal dependence of the incidence angle, these errors do not cancel out when monthly means are computed.

A correction method is developed on the basis of simulated data and tested successfully with measured data. Observed brightness temperature differences between DMSP F10 and F11 are reduced when using corrected data. If diurnal variations of geophysical parameters are investigated, the incidence angle correction is mandatory to obtain useful results, especially for DMSP F10.

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Ulrich Löhnert, Susanne Crewell, and Clemens Simmer

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.

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Prabhat K. Koner, Alessandro Battaglia, and Clemens Simmer

Abstract

A dynamic regularization scheme for rain-rate retrievals from attenuated radar measurements is presented. Most regularization techniques, including the optimal estimation method, use the state-space parameters to regularize the problem, which will always lead to a bias in the solution. To avoid this problem the authors introduce an evolutionary regularization technique, which is based on the spatial derivative of the measured reflectivity profile and allows for a bias-free global solution. The regularization strength is determined by the quadratic eigenvalue solution using the regularized total least squares method. With the new method, the authors perform a retrieval of rain-rate profiles from simulated measurements of a nadir-pointing W-band (94 GHz) radar, in a configuration similar to the cloud radar employed on CloudSat. The simulations assume that multiple scattering is negligible and only liquid hydrometeors are taken into account. The authors compare the results of this method with the outcome of an optimal estimation method and demonstrate that their method is superior in terms of reliability, correlation coefficient, and dispersion to the optimal estimation method for layers experiencing high values of attenuation; therefore, the a priori bias typical for optimal estimation solutions is avoided.

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Quanhua Liu, Clemens Simmer, and Eberhard Ruprecht

Abstract

A neural network is used to calculate the longwave net radiation (L net) at the sea surface from measurements of the Special Sensor Microwave/Imager (SSM/I). The neural network applied in this study is able to account largely for the nonlinearity between L net and the satellite-measured brightness temperatures (TB). The algorithm can be applied for instantaneous measurements over oceanic regions with the area extent of satellite passive microwave observations (30–60 km in diameter). Comparing with a linear regression method the neural network reduces the standard error for L net from 17 to 5 W m−2 when applied to model results. For clear-sky cases, a good agreement with an error of less than 5 W m−2 for L net between calculations from SSM/I observations and pyrgeometer measurements on the German research vessel Poseidon during the International Cirrus Experiment (ICE) 1989 is obtained. For cloudy cases, the comparison is problematic due to the inhomogenities of clouds and the low and different spatial resolutions of the SSM/I data. Global monthly mean values of L net for October 1989 are computed and compared to other sources. Differences are observed among the climatological values from previous studies by H.-J. Isemer and L. Hasse, the climatological values from R. Lindau and L. Hasse, the values of W. L. Darnell et al., and results from this study. Some structures of L net are similar for results from W. L. Darnell et al. and the present authors. The differences between both results are generally less than 15 W m−2. Over the North Atlantic Ocean the authors found a poleward increase for L net, which is contrary to the results of H.-J. Isemer and L. Hasse.

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Susanne Crewell, Eberhard Ruprecht, and Clemens Simmer

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.

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Malte Diederich, Alexander Ryzhkov, Clemens Simmer, Pengfei Zhang, and Silke Trömel

Abstract

In a series of two papers, rain-rate retrievals based on specific attenuation A at radar X-band wavelength using the R(A) method presented by Ryzhkov et al. are thoroughly investigated. Continuous time series of overlapping measurements from two polarimetric X-band weather radars in Germany during the summers of 2011–13 are used to analyze various aspects of the method, like miscalibration correction, ground clutter contamination, partial beam blockage (PBB), sensitivity to precipitation characteristics, and sensitivity to temperature assumptions in the retrievals. In Part I of the series, the relations inherent to the R(A) method were used to calculate radar reflectivity Z from specific attenuation and it was compared with measured reflectivity to estimate PBB and calibration errors for both radars. In this paper, R(A) rain estimates are compared to R(Z) and R(K DP) retrievals using specific phase shift K DP. PBB and calibration corrections derived in Part I made the R(Z) rainfall estimates almost perfectly consistent. Accumulated over five summer months, rainfall maps showed strong effects of clutter contamination if R(K DP) is used and weaker impact on R(A). These effects could be reduced by processing the phase shift measurements with more resilience toward ground clutter contamination and by substituting problematic R(K DP) or R(A) estimates with R(Z). Hourly and daily accumulations from rain estimators are compared with rain gauge measurements; the results show that R(A) complemented by R(Z) in segments with low total differential phase shift correlates best with gauges and has the lowest bias and RMSE, followed by R(K DP) substituted with R(Z) at rain rates below 8 mm h−1.

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Malte Diederich, Alexander Ryzhkov, Clemens Simmer, Pengfei Zhang, and Silke Trömel

Abstract

In a two-part paper, radar rain-rate retrievals using specific attenuation A suggested by Ryzhkov et al. are thoroughly investigated. Continuous time series of overlapping measurements from two twin polarimetric X-band weather radars in Germany during the summers of 2011–13 are used to analyze various aspects of rain-rate retrieval, including miscalibration correction, mitigation of ground clutter contamination and partial beam blockage (PBB), sensitivity to precipitation characteristics, and the temperature assumptions of the R(A) technique. In this paper, the relations inherent to the R(A) method are used to estimate radar reflectivity Z from A and compare it to the measured Z in order to estimate PBB and calibration offsets for both radars. The fields of Z estimated from A for both radars are consistent, and the differences between Z(A) and measured Z are in good agreement with the ones calculated using either consistency relations between reflectivity at horizontal polarization Z H, differential reflectivity Z DR, and specific differential phase K DP in rain or a digital elevation model in the presence of PBB. In the analysis, the dependence of A on temperature appears to have minimal effects on the overall performance of the method. As expected, the difference between Z(A) and attenuation-corrected measured Z observations varies with rain type and exhibits a weak systematic dependency on rainfall intensity; thus, averaging over several rain events is required to obtain reliable estimates of the Z biases caused by radar miscalibration and PBB.

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Luying Ji, Xiefei Zhi, Clemens Simmer, Shoupeng Zhu, and Yan Ji

Abstract

We analyzed 24-h accumulated precipitation forecasts over the 4-month period from 1 May to 31 August 2013 over an area located in East Asia covering the region 15.05°–58.95°N, 70.15°–139.95°E generated with the ensemble prediction systems (EPS) from ECMWF, NCEP, UKMO, JMA, and CMA contained in the TIGGE dataset. The forecasts are first evaluated with the Method for Object-Based Diagnostic Evaluation (MODE). Then a multimodel ensemble (MME) forecast technique that is based on weights derived from object-based scores is investigated and compared with the equally weighted MME and the traditional gridpoint-based MME forecast using weights derived from the point-to-point metric, mean absolute error (MAE). The object-based evaluation revealed that attributes of objects derived from the ensemble members of the five individual EPS forecasts and the observations differ consistently. For instance, their predicted centroid location is more southwestward, their shape is more circular, and their orientation is more meridional than in the observations. The sensitivity of the number of objects and their attributes to methodological parameters is also investigated. An MME prediction technique that is based on weights computed from the object-based scores, median of maximum interest, and object-based threat score is explored and the results are compared with the ensemble forecasts of the individual EPS, the equally weighted MME forecast, and the traditional superensemble forecast. When using MODE statistics for the forecast evaluation, the object-based MME prediction outperforms all other predictions. This is mainly because of a better prediction of the objects’ centroid locations. When using the precipitation-based fractions skill score, which is not used in either of the weighted MME forecasts, the object-based MME forecasts are slightly better than the equally weighted MME forecasts but are inferior to the traditional superensemble forecast that is based on weights derived from the point-to-point metric MAE.

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Katharina Schinagl, Petra Friederichs, Silke Trömel, and Clemens Simmer

Abstract

A suitable formulation of the rain drop size distribution (DSD) is a prerequisite for a successful assimilation of radar polarimetric information on rain into a numerical weather prediction model. Popular DSD parameterizations in two-moment bulk microphysics schemes use relations between the so-called mean-mass diameter and the DSD shape parameter μ, in order to prevent overly strong size sorting in the models. In radar polarimetry constrained-gamma DSDs with empirical relations between the shape and scale parameter are commonly used. This study compares the different DSD formulations and highlights the differences. Synthetic polarimetric radar observations for X band (9.39 GHz) and S band (3 GHz) were calculated from the different DSDs using the T-matrix method. Depending on the constraint that is assumed for the DSDs, the polarimetric moments exhibit quite different dependencies on the mean diameter, which are particularly striking for differential reflectivity Z DR. To successfully assimilate observed polarimetric moments into atmospheric models, formulations—possibly more flexible than those investigated in this study—have to be found that sufficiently represent microphysical processes and at the same time are consistent with empirical relations derived from disdrometer and radar polarimetric measurements.

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Silke Trömel, Alexander V. Ryzhkov, Pengfei Zhang, and Clemens Simmer

Abstract

Backscatter differential phase δ within the melting layer has been identified as a reliably measurable but still underutilized polarimetric variable. Polarimetric radar observations at X band in Germany and S band in the United States are presented that show maximal observed δ of 8.5° at X band but up to 70° at S band. Dual-frequency observations at X and C band in Germany and dual-frequency observations at C and S band in the United States are compared to explore the regional frequency dependencies of the δ signature. Theoretical simulations based on usual assumptions about the microphysical composition of the melting layer cannot reproduce the observed large values of δ at the lower-frequency bands and also underestimate the enhancements in differential reflectivity Z DR and reductions in the cross-correlation coefficient ρ . Simulations using a two-layer T-matrix code and a simple model for the representation of accretion can, however, explain the pronounced δ signatures at S and C bands in conjunction with small δ at X band. The authors conclude that the δ signature bears information about microphysical accretion and aggregation processes in the melting layer and the degree of riming of the snowflakes aloft.

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