RETRIEVING OVER-OCEAN PRECIPITABLE WATER FROM THE SSM/I MEASUREMENTS WITH SEVERAL REGRESSION ALGORITHMS

A collocated SSM/I and radiosonde measurement data set provided by the NASDA(Japan)was used to retrieve the total precipitable water(PW)over oceans.The retrieval results obtainedwith several regression algorithms were compared against the radiosonde measurements.It isshown that:(a)the routinely operational algorithm of Alishouse et al.(1990)yields significantunderestimation in high PW regime and overestimation in low PW regime;(b)a cubic correctionby Colton and Poe(1994)is not sufficient and globally improves slightly the retrieval results;and(c)the regression algorithm with the form of brightness temperature(T_b)function In (280-T_b)gives a little largely scattered retrievals in whole PW range but without considerable over-andunderestimates in low and high PW regimes.To improve the estimation of the oceanic precipitablewater from the SSM/I measurements,a composite algorithm with different forms of T_b function inlow.medium and high PW regimes is proposed and tested.     

RETRIEVING OVER-OCEAN PRECIPITABLE WATER FROM THE SSM/I MEASUREMENTS WITH SEVERAL REGRESSION ALGORITHMS*

A collocated SSM/I and radiosonde measurement data set provided by the NASDA(Japan) was used to retrieve the total precipitable water(PW) over oceans.The retrieval results obtained with several regression algorithms were compared against the radiosonde measurements.It is shown that:(a) the routinely operational algorithm of Alishouse et al.(1990) yields significant underestimation in high PW regime and overestimation in low PW regime;(b) a cubic correction by Colton and Poe(1994) is not sufficient and globally improves slightly the retrieval results;and(c) the regression algorithm with the form of brightness temperature(T_b) function In(280-T_b) gives a little largely scattered retrievals in whole PW range but without considerable over-and underestimates in low and high PW regimes.To improve the estimation of the oceanic precipitable water from the SSM/I measurements,a composite algorithm with different forms of T_b function in low.medium and high PW regimes is proposed and tested.     

星载被动微波遥感反演降水算法回顾

根据不同的降水反演方法对多种利用SSM／I、TMI反演降水的算法进行归类总结，按下垫面检测和降水反演算法两大步骤进行简要描述，并分经验法、半经验(半物理)法、物理模式法及物理廓线法4类方法对多种算法作了回顾。     

Rain rate estimation from a synergetic use of SSM/I, AVHRR and meso-scale numerical model data

Summary In this paper a retrieval technique for estimating rainfall rates is introduced. The novel feature of this technique is the combination of two satellite radiometers — the Special Sensor Microwave/Imager (SSM/I) and the Advanced Very High-Resolution Radiometer (AVHRR) — with mesoscale weather prediction model data. This offers an adjustment of the model atmospheres to reality which is necessary for calculating brightness temperatures that can be compared with microwave satellite measurements.In sensitivity studies it was found that the estimation of precipitation is determined to a high degree by the particle size distribution of rain and snow, especially by the size distribution of solid hydrometeors. These studies also reveal the influence of the knowledge of the correct cloud coverage inside a SSM/I pixel and the importance of using a realistic temperature profile instead of using standard atmospheres.The retrieval technique is based on radiative transfer calculations using the model of Kummerow et al. (1989). The algorithm consists of two parts: First Guess (FG) brightness temperatures for the SSM/I frequencies are generated as a function of the cloud top height and the cloud coverage, derived from AVHRR data and predictions from a meso-scale model. The rainfall rate of different types of clouds containing raindrops, ice particles and coexisting ice and water hydrometeors is then calculated as a function of the cloud top height. As an example, a strong convective rain event over the western part of Europe and over the Alps is taken to evaluate the performance of this technique. Good agreement with radar data from the German Weather Service was achieved. Compared to statistical rainfall algorithms, the current algorithm shows a better performance of detecting rainfall areas.With 12 Figures     

Resolving SSM/I-ship radar rainfall discrepancies from AIP-3

The third algorithm intercomparison project （AIP-3） involved rain estimates from more than 50 satellite rainfall algorithms and ground radar measurements within the Intensive Flux Array （IFA） over the equatorial western Pacific warm pool region during the Tropical Ocean Global Atmosphere coupled Ocean-Atmosphere Response Experiment （TOGA COARE）. Early results indicated that there was a sys- tematic bias between rainrates from satellite passive microwave and ground radar measurements. The mean rainrate from radar measurements is about 50% underestimated compared to that from passive microwave-based retrieval algorithms. This paper is designed to analyze rain patterns from the Florida State University rain retrieval algorithm and radar measurements to understand physically the rain discrep- ancies. Results show that there is a clear range-dependent bias associated with the radar measurements. However, this range-dependent systematical bias is almost eliminated with the corrected radar rainrates. Results suggest that the effects from radar attenuation correction, calibration and beam filling are the major sources of rain discrepancies. This study demonstrates that rain retrievals based on satellite mea- surements from passive microwave radiometers such as the Special Sensor of Microwave Imager （SSM/I） are reliable, while rain estimates from ground radar measurements are correctable.     

A comparison of SSM/I and TOVS column water vapor data over the global oceans

Summary This paper presents a comparison of column water vapor (CWV) information derived from both infrared measurements as part of the TIROS-N Operational Vertical Sounder (TOVS) and Special Sensor Microwave/Imager (SSM/I) in an attempt to assess the relative merits of each kind of data. From the analyses presented in this paper, it appears that both types of satellite data closely reproduce the bulk climatological relationships introduced in earlier studies using different data. This includes both the bulk relationship between CWV and the sea surface temperature and the annual variation of CWV over the world's oceans. The TOVS water vapor data tends to be systematically smaller than the SSM/I data and when averaged over the ocean covered regions of the globe this difference is between 2–3 kgm^–2. Using a cloud liquid water threshold technique to establish clear sky values of SSM/I water vapor, we conclude that the differences between TOVS and SSM/I are largely a result of the clear sky bias in TOVS sampling except in the subsidence regions of the subtropics. The clear sky bias is considerably smaller than previously reported and we attribute this improvement to the new physical retrieval scheme implemented by NOAA NESDIS. While there is considerable agreement between the two types of satellite data, there are also important differences. In regions where there is drying associated with large scale subsidence of the atmosphere, the TOVS CWV's are too moist relative to both radiosonde and SSM/I data and this difference may exceed 10 kgm^–2. The explanation for this difference lies in the limitations of infrared radiative transfer. By contrast, in regions of deep convection, such as in the ITCZ, TOVS CWV is systematically lower than the SSM/I CWV. Both TOVS and SSM/I data demonstrate similar kinds of gross effects of large scale circulation on the water vapor except in these subsidence regions where TOVS data leads to an under-prediction of the effects of subsidence drying.With 11 Figures     

Design of an inversion-based precipitation proflie retrieval algorithm using an explicit cloud model for initial guess microphysics

Summary This paper describes the design and validation of the FSU precipitation profile retrieval algorithm for applications with SSM/I passive microwave measurements. The algorithm employs the principles of multifrequency inversion based on forward radiative transfer modeling. A Sobolev 2-stream solution to the radiative transfer equation (RTE) is used as the forward RTE model and is described herein. The method is shown to be very accurate, retaining the same degree of computational efficiency inherent to simpler 2-stream flux models. Tests of the model against more detailed multistream, adding-doubling models demonstrate that the Sobolev solution produces radiance accuracies of approximately 1%. An advantage of the Sobolev approach is that the intensity field can be expanded in a mathematically consistent fashion, an essential feature in applications with the off-nadir SSM/I microwave measurements. A 4-dimensional non-hydrostatic cloud model provides the microphysical underpinnings of the algorithm, and is used to generate the initial guess profiles for the inversion procedure. The various stages of the algorithm are described, as well as two different methods of computational implementation for storm-scale and global-scale applications. The paper also summarizes a number of different rainrate validation analyses that have been carried out at the two scales, as well as examining the capabilities of the algorithm in diagnosing the vertical latent heating structure. The validation results represent a mixture of quantitative comparisons to radar and raingage datasets, and more qualitative comparisons to the numerical modeling results of other investigators. Because of known uncertainties in the validation data in terms of their accuracy and representativeness, and the underlying problems with time-space matching of the comparisons, it is not yet possible to place absolute confidence limits on the retrievals. However, taken as a whole, the rainrate validation analyses and the estimated latent heating profiles present solid evidence that the profile approach is returning credible rainfall estimates whose uncertainnes are commensurate with those of current validation data.With 12 Figures     

Physical retrievals of over-ocean rain rate from multichannel microwave imagery. Part II: Algorithm implementation

Summary A new physical inversion-based algorithm for retrieving rain rate over the ocean with the Special Sensor Microwave Imager (SSM/I) is described. In a departure from other rain rate retrieval algorithms, the satellite observables inverted in the present algorithm are not the raw brightness temperatures but rather normalized polarizations for 19.35, 37.0, and 85.5 GHz, plus an 85.5 GHz scattering index which is sensitive primarily to ice particles aloft. The normalized polarizations are interpreted as footprint-averages of theoretically derived analytic functions of the column optical depth associated primarily with liquid water. The effective vertical depth of the rain layer is specified as a function of the SSM/I estimated column water vapor.The retrieval algorithm performs an iterative search for a high resolution (12.5 km) rain field which is simultaneously consistent with the 19.35 and 37.0 GHz normalized polarizations. The first-guess rain rate field is supplied by the 85.5 GHz scattering index. At gridpoints for which the rain column optical depth exceeds the dynamic range of the attenuation-based indices, the first-guess field is left essentially unmodified; elsewhere, the required consistency with the 19 and 37 GHz indices usually results in significant modification of the scattering-based rain rate estimates.The algorithm as described here is a prototype implementation which was developed with reference only to idealized theoretical models; empirical improvements to the numerical scheme and the model coefficients will be made in the future as results from the first Precipitation [algorithm] Intercomparison Project 1 (PIP-1) and the second phase of the GPCP (Global Precipitation Climatology Project) algorithm Intercomparison Project (AIP/2) are analyzed, as well as data from individual validation efforts. Although the algorithm is physically based and uses all SSM/I channels, it is computationally much less demanding than cloud/radiative transfer model-based inversion algorithms published else-where.With 9 Figures     

Intercomparison of the performance of MM5/WRF with and without satellite data assimilation in short-range forecast applications over the Indian region

The present study is conducted to verify the short-range forecasts from mesoscale model version5 (MM5)/weather research and forecasting (WRF) model over the Indian region and to examine the impact of assimilation of quick scatterometer (QSCAT) near surface winds, spectral sensor microwave imager (SSM/I) wind speed and total precipitable water (TPW) on the forecasts by these models using their three-dimensional variational (3D-Var) data assimilation scheme for a 1-month period during July 2006. The control (without satellite data assimilation) as well as 3D-Var sensitivity experiments (with assimilating satellite data) using MM5/WRF were made for 48 h starting daily at 0000 UTC July 2006. The control run is analyzed for the intercomparison of MM5/WRF short-range forecasts and is also used as a baseline for assessing the MM5/WRF 3D-Var satellite data sensitivity experiments. As compared to the observation, the MM5 (WRF) control simulations strengthened (weakened) the cross equatorial flow over southern Arabian sea near peninsular India. The forecasts from MM5 and WRF showed a warm and moist bias at lower and upper levels with a cold bias at the middle level, which shows that the convective schemes of these models may be too active during the simulation. The forecast errors in predicted wind, temperature and humidity at different levels are lesser in WRF as compared to MM5, except the temperature prediction at lower level. The rainfall pattern and prediction skill from day 1 and day 2 forecasts by WRF is superior to MM5. The spatial distribution of forecast impact for wind, temperature, and humidity from 1-month assimilation experiments during July 2006 demonstrated that on average, for 24 and 48-h forecasts, the satellite data improved the MM5/WRF initial condition, so that model errors in predicted meteorological fields got reduced. Among the experiments, MM5/WRF wind speed prediction is most benefited from QSCAT surface wind and SSM/I TPW assimilation while temperature and humidity prediction is mostly improved due to latter. The largest improvement in MM5/WRF rainfall prediction is due to the assimilation of SSM/I TPW. The assimilation of SSM/I wind speed alone in MM5/WRF degraded the humidity and rainfall prediction. In summary the assimilation of satellite data showed similar impact on MM5/WRF prediction; largest improvement due to SSM/I TPW and degradation due to SSM/I wind speed.     

Comparisons of a Chemical Transport Model with a Four-Year (April to September) Analysis of Fine- and Coarse-Mode Aerosol Optical Depth Retrievals Over the Canadian Arctic

We compared April to September retrievals of total, fine-mode (sub-micron), and coarse-mode (super-micron) aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET) with simulations from a global three-dimensional chemical transport model, the Goddard Earth Observing System (GEOS-Chem), across five Arctic stations and a four-year sampling period. It was determined that the AOD histograms of both the retrievals and the simulations were better represented by a lognormal distribution and that the successful simulation of this empirical feature as well as its consequences (including a better model versus retrieval coefficient of determination in log-log AOD space) represented a general indicator of model evaluation success. Seasonal (monthly averaged) AOD retrievals were sensitive to the way in which the averaging was performed; this was ascribed to the presence of highly variable fine-mode smoke in the western Arctic. The retrieved and modelled station-by-station fine-mode AOD averages showed a peak in April/May that decreased over the summer, while the model underestimated the fine-mode AOD by an average of about 0.004 (～6%). Both the retrievals and simulations showed seasonal coarse-mode AOD variations with a peak in April/May that was attributed to Asian and/or Saharan dust. The model's success in capturing such weak seasonal events helps to confirm the relevance of the separation of the fine and coarse modes and the general validity of model estimates in the Arctic.     

ERROR COMPARISON OF WIND FIELD RETRIEVAL FROM SINGLE AND DUAL-DOPPLER RADAR OBSERVATIONS

The error distributions of the wind fields retrieved from single and dual-Doppler radar observations are given inthis paper.The results indicate that the error of dual-Doppler retrieval depends on the position in the scan region of thedual-Doppler radar.The error of single-Doppler retrieval by using velocity azimuth processing(VAP)technique de-pends on the angle between the directions of wind and the radar beam.Generally,the winds retrieved from single Doppl-er radar are close to those retrieved from dual-Doppler radar.But,the error distribution of the single-Doppler retrievalis different from the dual-Doppler retrieval.We simulate the retrievals of single Doppler observation by the use of theoutput wind data from a 3-D numerical model of severe convection.The comparison of the simulated single-anddual-Doppler retrievals shows that the VAP may be a suitable technique for the operational analysis of mesoscale windfields.It can also be used as a supplement to wind field retrieval in the field experiment.     

Raindrop Size Distribution Parameters Retrieved from Guangzhou S-band Polarimetric Radar Observations

According to the statistical shape–slope (μ–Λ) relationship observed for the first time by several 2D-Video-Distrometers (2DVD) in southern China, a constrained gamma (C-G) model was proposed for the retrieval of rain drop size distributions (DSDs) from Guangzhou S-band polarimetric radar observations. Two typical precipitation processes were selected to verify the accuracy of the retrieval scheme. The μ–Λ relationship: Λ = 0.0241μ² + 0.867μ + 2.453 was obtained based on the 2DVD observation results from at Huizhou Longmen station, which is a very representative location in the area. Relying on the Guangzhou polarimetric radar measurements of radar reflectivity (Z_HH) and differential reflectivity (Z_DR), the gamma (Γ) size distribution parameters (N₀, μ, and Λ) can be retrieved by the C-G model retrieval scheme. The results show that the Guangzhou polarimetric radar retrievals of DSDs were close to the 2DVD observations at Guangzhou Maofengshan station. The rain rate, mass mean diameter, and normalized intercept parameter of radar retrievals were in good agreement with the 2DVD observations, and the relative errors were less than 10%. The overall accuracy of the retrieval scheme was high. The retrieval scheme has established the relationship between the polarimetric radar measurements and gamma size distribution parameters. It will be helpful to in-depth research and application of the dual-polarization radar data in microphysical precipitation processes analysis, as well as convection-resolved numerical model data assimilation and prediction effect evaluation.     

Ensemble retrieval of atmospheric temperature profiles from AIRS

ABSTRACT Satellite-based observations provide great opportunities for improving weather forecasting. Physical retrieval of atmo spheric profiles from satellite observations is sensitive to the uncertainty of the first guess and other factors. In order to improve the accuracy of the physical retrieval, an ensemble methodology was developed with an emphasis on perturbing the first guess. In the methodology, a normal probability density function （PDF） is used to select the optimal profile from the ensemble retrievals. The ensemble retrieval algorithm contains four steps： （1） regression retrieval for original first guess; （2） perturbation of the original first guess to generate new first guesses （ensemble first guesses）; （3） using the ensemble first guesses and nonlinear iterative physical retrieval to generate ensemble physical results; and （4） the final optimal profile is selected from the ensemble physical results by using PDE Temperature eigenvectors （EVs） were used to generate the pertur- bation and generate the ensemble first guess. Compared with the regular temperature profile retrievals from the Atmospheric InfraRed Sounder （AIRS）, the ensemble retrievals RMSE of temperature profiles selected by the PDF was reduced between 150 and 320 hPa and below 400 hPa, with a maximum improvement of 0.3 K at 400 hPa. The bias was also reduced in many layers, with a maximum improvement of 0.69 K at 460 hPa. The combined optimal （CombOpt） profile and a mean optimal （MeanOpt） profile of all ensemble physical results were improved below 150 hPa. The MeanOpt profile was better than the CombOpt profile, and was regarded as the final optimal （FinOpt） profile. This study lays the foundation for improving temperature retrievals from hyper-spectral infrared radiance measurements.     

Application of global snow model for the estimation of snow depth in the UK

Microwave imagery can be used successfully for mapping of snow and estimation of snow pack characteristics under almost all weather conditions. This research is a contribution to the field of space borne remote sensing of snow by means of passive microwave data imagery. The satellite data are acquired from the Special Sensor Microwave Imager (SSM/I). The SSM/I is a four frequency seven channels dual polarization (except 22 GHz which is only vertically polarized) scanning radiometer with channels located at 19, 22, 37, and 85 GHz frequencies. A radiative transfer theory based model is used to estimate the snow cover characteristics of different snow pack types in the UK. A revised form of the Chang et al. (Nord Hydrol 16:57–66, 1987) model is used for this purpose. The revised Chang model was calibrated for global snow monitoring and takes into account forest fractional coverage effects. Snow cover characteristics have significant effects on up-welling naturally emitted microwave radiation through the processes of forward scattering. The up-welling signal is more complex for snow covers that consist of free liquid water content. The aim of this study is to test the global snow depth model for the UK snow cover. The Chang model predicted snow depth bias results for January, February, and March 1995 are ?1.26, ?0.35, and ?0.63 cm, respectively. Similarly, the Chang model Mean Absolute Error (MAE) for January, February, and March 1995 have values 2.88, 2.38, and 1.91 cm, respectively. These results show that the Chang model underestimates the snow depth prediction for all the case studies. The results of this study led us to the conclusion that the global snow models (Chang model) when applied for the retrieval of local snow depth estimation (UK snow cover) underestimate snow depth.     

Nonlinear Retrieval of Atmospheric Ozone Profile from Solar Backscatter Ultraviolet Measurements:Theory and Simulation

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Validation of IASI-Retrieved Atmospheric Water Vapor Data over the Tibetan Plateau Region

The Infrared Atmospheric Sounding Interferometer (IASI) is a new-generation ultraspectral atmospheric sounding instrument mounted on the MetOp-A, the first operational polar-orbiting satellite developed by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). It is an ultrahigh spectral-resolution atmospheric detector which can detect atmospheric chemical composition, temperature, and humidity profiles with high accuracy and resolution. In the present study, through comparative analyses of the similarities and differences between the IASI and the radiosonde observation (RAOB) water vapor data, and between the IASI and the Aqua-AIRS water vapor retrievals, a detailed and systematic assessment of the credibility of the IASI water vapor retrievals over the plateau region was made. A comparison of the IASI retrievals with the AIRS retrievals and the RAOB measurements over the Tibetan Plateau revealed that the IASI retrieval data are reliable and can be used for conducting further studies.     

利用SSM/I数据判识我国及周边地区雪盖

积雪参数是气候学和水文学研究中所需的重要物理量, 确保积雪参数测定的准确性与及时性对于气候学研究、水文应用以及防灾减灾都非常重要。利用微波数据可获取有云存在时的积雪覆盖图, 遥感雪深和雪水当量信息。采用微波数据判识雪盖并得到积雪状态 (干、湿) 信息, 不仅可以弥补利用光学遥感数据判识雪盖的不足之处, 而且也是利用微波数据反演雪深和雪水当量参数必需的先期工作。该文介绍利用SSM/I的多频双极化微波数据开展我国及周边地区积雪判识方法研究的结果。分析国外全球判识方法的雪盖判识结果指出, 国外算法易在青藏高原等地区将冻土误判为积雪, 造成雪盖面积的偏高估计。研究给出了在我国及周边地区 (17°~57°N, 65°~145°E) 利用SSM/I数据判识积雪的改进方法, 在完成积雪判识的同时还给出了雪深和积雪状态的定性信息, 与已有全球雪盖判识方法相比有较大改进, 大大减小了青藏高原等地区冻土对积雪判识的影响。     

数据同化/卫星反演/数值预报相互循环作用系统

介绍数据同化／卫星反演／数值预报相互作用的一种方案，将原来相互独立进行的操作组成为相互作用的循环系统。国家卫星气象中心（ＮＳＭＣ）和国家气象中心（ＮＭＣ）联合开发这个项目，其目的是改善我国卫星探测反演及其在业务数值预报中应用的情况。该文首先介绍相互作用循环流物基本原理，以及的分析／预报模式和卫星反演的方案，然后给出国家卫星气象中心和国家气象中心联合试验的阶段性成果。研究试验表明，相互循环作用方案有     

Impact of assimilation of conventional and satellite meteorological observations on the numerical simulation of a Bay of Bengal Tropical Cyclone of November 2008 near Tamilnadu using WRF model

The objective of this study is to examine the impact of assimilation of conventional and satellite data on the prediction of a severe cyclonic storm that formed in the Bay of Bengal during November 2008 with the four-dimensional data assimilation (FDDA) technique. The Weather Research and Forecasting (WRF ARW) model was used to study the structure, evolution, and intensification of the storm. Five sets of numerical simulations were performed using the WRF. In the first one, called Control run, the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) was used for the initial and boundary conditions. In the remaining experiments available observations were used to obtain an improved analysis and FDDA grid nudging was performed for a pre-forecast period of 24 h. The second simulation (FDDAALL) was performed with all the data of the Quick Scatterometer (QSCAT), Special Sensor Microwave Imager (SSM/I) winds, conventional surface, and upper air meteorological observations. QSCAT wind alone was used in the third simulation (FDDAQSCAT), the SSM/I wind alone in the fourth (FDDASSMI) and the conventional observations alone in the fifth (FDDAAWS). The FDDAALL with assimilation of all observations, produced sea level pressure pattern closely agreeing with the analysis. Examination of various parameters indicated that the Control run over predicted the intensity of the storm with large error in its track and landfall position. The assimilation experiment with QSCAT winds performed marginally better than the one with SSM/I winds due to better representation of surface wind vectors. The FDDAALL outperformed all the simulations for the intensity, movement, and rainfall associated with the storm. Results suggest that the combination of land-based surface, upper air observations along with satellite winds for assimilation produced better prediction than the assimilation with individual data sets.     

Use of total precipitable water classification of a priori error and quality control in atmospheric temperature and water vapor sounding retrieval

This study investigates the use of dynamic a priori error information according to atmospheric moistness and the use of quality controls in temperature and water vapor profile retrievals from hyperspectral infrared (IR) sounders. Temperature and water vapor profiles are retrieved from Atmospheric InfraRed Sounder (AIRS) radiance measurements by applying a physical iterative method using regression retrieval as the first guess. Based on the dependency of first-guess errors on the degree of atmospheric moistness, the a priori first-guess errors classified by total precipitable water (TPW) are applied in the AIRS physical retrieval procedure. Compared to the retrieval results from a fixed a priori error, boundary layer moisture retrievals appear to be improved via TPW classification of a priori first-guess errors. Six quality control (QC) tests, which check non-converged or bad retrievals, large residuals, high terrain and desert areas, and large temperature and moisture deviations from the first guess regression retrieval, are also applied in the AIRS physical retrievals. Significantly large errors are found for the retrievals rejected by these six QCs, and the retrieval errors are substantially reduced via QC over land, which suggest the usefulness and high impact of the QCs, especially over land. In conclusion, the use of dynamic a priori error information according to atmospheric moistness, and the use of appropriate QCs dealing with the geographical information and the deviation from the first-guess as well as the conventional inverse performance are suggested to improve temperature and moisture retrievals and their applications.