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Retrieval schemes often use two important components: 1) a radiative transfer model (RTM) inside the retrieval procedure or to construct the learning dataset for the training of the statistical retrieval algorithms and 2) a numerical weather prediction (NWP) model to provide a first guess or, again, to construct a learning dataset. This is particularly true in operational centers. As a consequence, any physical retrieval or similar method is limited by inaccuracies in the RTM and NWP models on which it is based. In this paper, a method for partially compensating for these errors as part of the sensor calibration is presented and evaluated. In general, RTM/NWP errors are minimized as best as possible prior to the training of the retrieval method, and then tolerated. The proposed method reduces these unknown and generally nonlinear residual errors by training a separate preprocessing neural network (NN) to produce calibrated radiances from real satellite data that approximate those radiances produced by the ''flawed'' NWP and RTM models. The final ''compensated/flawed'' retrieval assures better internal consistency of the retrieval procedure and then produces more accurate results. To the authors'' knowledge, this type of NN model has not been used yet for this purpose. The calibration approach is illustrated here on one particular application: the retrieval of atmospheric water vapor from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Humidity Sounder for Brazil (HSB) measurements for nonprecipitating scenes, over land and ocean. Before being inverted, the real observations are ''projected'' into the space of the RTM simulation space from which the retrieval is designed. Validation of results is performed with radiosonde measurements and NWP analysis departures. This study shows that the NN calibration of the AMSR-E/HSB observations improves water vapor inversion, over ocean and land, for both clear and cloudy situations. The NN calibration is efficient and very general, being applicable to a large variety of problems. The nonlinearity of the NN allows for the calibration procedure to be state dependent and adaptable to specific cases (e.g., the same correction will not be applied to medium-range measurement and to extreme conditions). Its multivariate nature allows for a full exploitation of the complex correlation structure among the instrument channels, making the calibration of each single channel more robust. The procedure would make it possible to project the satellite observations in a reference observational space defined by radiosonde measurements, RTM simulations, or other instrument observational space. [ABSTRACT FROM AUTHOR]

Data Collection 5 processing for the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Earth Observing System (EOS) Terra and Aqua spacecraft includes an algorithm for detecting multilayered clouds in daytime. The main objective of this algorithm is to detect multilayered cloud scenes, specifically optically thin ice cloud overlying a lower-level water cloud, that present difficulties for retrieving cloud effective radius using single-layer plane-parallel cloud models. The algorithm uses the MODIS 0.94- μm water vapor band along with CO2 bands to obtain two above-cloud precipitable water retrievals, the difference of which, in conjunction with additional tests, provides a map of where multilayered clouds might potentially exist. The presence of a multilayered cloud results in a large difference in retrievals of above-cloud properties between the CO2 and the 0.94- μm methods. In this paper the MODIS multilayered cloud algorithm is described, results of using the algorithm over example scenes are shown, and global statistics for multilayered clouds as observed by MODIS are discussed. A theoretical study of the algorithm behavior for simulated multilayered clouds is also given. Results are compared to two other comparable passive imager methods. A set of standard cloudy atmospheric profiles developed during the course of this investigation is also presented. The results lead to the conclusion that the MODIS multilayer cloud detection algorithm has some skill in identifying multilayered clouds with different thermodynamic phases. [ABSTRACT FROM AUTHOR]

Convective available potential energy (CAPE) is one of the physical quantities used by operational meteorologists when issuing severe-weather convective watches and warnings. Recent advances in satellite technology could provide timely observations of atmospheric temperature and water vapor profiles over the continental United States, but only limited validation exists in the literature to characterize uncertainties in CAPE derived from the new satellite sensors. In this study, 10 years of Vaisala, Inc., RS92 radiosonde observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site were matched to overpasses of the NASA Aqua satellite that were made from January 2005 through December 2014. Vertical profiles of temperature and water vapor from the NASA Atmospheric Infrared Sounder (AIRS) were extracted in a region surrounding the DOE ARM SGP central facility near Lamont, Oklahoma. Surface-based CAPE was computed using software consistent with methods used by the National Weather Service Storm Prediction Center. The one-to-one correspondence of the AIRS-derived CAPE with the ARM-radiosonde-derived CAPE has a correlation coefficient of only 0.34. Substitution of the ARM-radiosonde surface values into the AIRS profiles improves the correlation to 0.95. The use of AIRS profiles above the surface level provides surface-based CAPE values that are very similar to those computed from Vaisala radiosondes. These results suggest that a merging of surface observations with satellite-derived thermodynamic profiles could make better use of the satellite spatial coverage and temporal sampling for estimation of CAPE in near-real time. [ABSTRACT FROM AUTHOR]

A new intersensor calibration scheme is developed for the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) to correct its scan-angle-dependent bias, the radar calibration beacon interference on the F-15 satellite, and other intersensor biases. The intersensor bias is characterized by the simultaneous overpass measurements with the F-13 SSM/I as a reference. This sensor data record (SDR) intersensor calibration procedure is routinely running at the National Oceanic and Atmospheric Administration and is now used for reprocessing all SSM/I environmental data records (EDR), including total precipitable water (TPW) and surface precipitation. Results show that this scheme improves the consistency of the monthly SDR''s time series from different SSM/I sensors. Relative to the matched rain products from the Tropical Rainfall Measuring Mission, the bias of SSM/I monthly precipitation is reduced by 12%% after intersensor calibration. TPW biases between sensors are reduced by 75%% over the global ocean and 20%% over the tropical ocean, respectively. The intersensor calibration reduces biases by 20.6%%, 15.7%%, and 6.5%% for oceanic, land, and global precipitation, respectively. The TPW climate trend is 1.59%% decade−−1 (or 0.34 mm decade−−1) for the global ocean and 1.39%% decade−−1 (or 0.63 mm decade−−1) for the tropical ocean, indicating related trends decrease of 38%% and 54%%, respectively, from the uncalibrated SDRs. Results demonstrate the large impacts of this calibration on the TPW climate trend. [ABSTRACT FROM AUTHOR]

Identifying and quantifying the intensity of light precipitation at global scales is still a difficult problem for most of the remote sensing algorithms in use today. The variety of techniques and algorithms employed for such a task yields a rather wide spectrum of possible values for a given precipitation event, further hampering the understanding of cloud processes within the climate. The ability of CloudSat’s millimeter-wavelength Cloud Profiling Radar (CPR) to profile not only cloud particles but also light precipitation brings some hope to the above problems. Introduced as version zero, the present work uses basic concepts of detection and retrieval of light precipitation using spaceborne radars. Based on physical principles of remote sensing, the radar model relies on the description of clouds and rain particles in terms of a drop size distribution function. Use of a numerical model temperature and humidity profile ensures the coexistence of mixed phases otherwise undetected by the CPR. It also provides grounds for evaluating atmospheric attenuation, important at this frequency. Related to the total attenuation, the surface response is used as an additional constraint in the retrieval algorithm. Practical application of the profiling algorithm includes a 1-yr preliminary analysis of global rainfall incidence and intensity. These results underscore once more the role of CloudSat rainfall products for improving and enhancing current estimates of global light rainfall, mostly at higher latitudes, with the goal of understanding its role in the global energy and water cycle. [ABSTRACT FROM AUTHOR]