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A few deficiencies remain in the current National Oceanic and Atmospheric Administration (NOAA) Integrated Calibration/Validation System (ICVS) noise calculation method for characterizing noise equivalent differential temperature $(NE\Delta T)$ performance of on-orbit Advanced Microwave Sounding Unit-A (AMSU-A) instruments. This ICVS method only accounts for the noise contribution resulting from one calibration parameter (warm count). The calculation is also dependent upon an assumption that the calibration gain equals to the sensitivity of warm count to temperature. In addition, the dependence of scene temperature is not considered. This study establishes a new methodology by accounting for the noise components resulting from all calibration parameters such as warm counts, warm target temperatures, space view (cold) counts, and their covariance. Each noise component is computed using the product of the overlapping Allan deviation of corresponding parameter and the sensitivity of Earth scene temperature to the parameter. The new method also comprises the variation of scene temperature via scene count in noise estimation. For the AMSU-A instruments aboard the NOAA-18 to NOAA-19 and Metop-A to Metop-C satellites, the magnitudes of the orbital average $NE\Delta T$ calculated using the ICVS method exceed those from the new method by approximately 8%–38%, corresponding to a range from 0.02 to 0.07 K. Particularly, the deviations at the upper temperature sounding channels from 10 to 14 are around 0.05 K. The magnitude of AMSU-A instrument noise can vary by around 18% for sounding channels and 40% for window channels due to scene temperature change. Therefore, the new method demonstrates its significant improvements in characterizing on-orbit AMSU-A instrument noise more accurately and comprehensively.

In this article, we first present a generalized methodology for deriving sensor brightness temperature sensor data records (SDR) from antenna temperature data records (TDR) applicable for Advanced Microwave Sounding Unit-A (AMSU-A) instruments. It includes corrections for antenna sidelobe contributions, antenna emission, and radiation perturbation due to the difference of Earth radiance in the main beam and that in the sidelobes that lie outside the main beam but within the Earth disk. For practical purposes, we simplify the methodology by neglecting the components other than the antenna sidelobe contributions to establish a consistent formulation with the legacy AMSU-A antenna pattern correction (APC) formula. The simplified formulation is then applied to the final AMSU-A instrument onboard the Metop-C satellite that was launched in November 2018, in order to compute APC coefficients for deriving SDR from TDR data. Furthermore, the performance of the calculated correction coefficients is validated by calculating the differences between the daily averaged AMSU-A (TDR and SDR) observations against radiative transfer model (O–B) simulations under clear sky conditions, and over open oceans. The validation results show that the derived temperature corrections are channel and scan position dependent, and can add 0.2–1.6 K to the antenna temperatures. In addition, the derived SDR O-B results exhibit a reduced and more symmetric scan angle-dependent bias when compared with corresponding TDR antenna temperatures.

Observations from the microwave sounding unit (MSU), from 1978 to 2006 and its successor, the Advanced Microwave Sounding Unit-A (AMSU-A, 1998–present), and onboard the National Oceanic and Atmospheric Administration’s polar-orbiting satellites have been widely used for estimating global climate trends. The MSU has a long-term data set, but it is difficult to obtain cloud information like the cloud liquid water path (LWP) directly from this data set. To monitor and investigate the cloud effect on global upper air temperature trends using MSU observations, a cloud detection algorithm must be developed for the MSU. Considering the similar center frequencies of MSU channel 1 and AMSU-A channel 3, a new cloud detection algorithm is established based on the differences between observed and model-simulated brightness temperatures ( $O$ - $B$ - $\mu $ ( $\alpha$ )- $\mu $ ( $\phi $ )) of MSU channel 1 or AMSU-A channel 3 over oceans, where $\mu $ ( $\phi$ ) is a latitudinal dependent global mean bias and $\mu $ ( $\alpha$ ) is the global mean bias depending on scan angle. If a data point satisfies the condition of $O$ - $B$ - $\mu $ ( $\alpha$ )- $\mu (\phi) \ge 1$ K, it is removed from the clear sky data set. In order to ensure that those points that are partially affected by the clouds, such as clear and cloud mixed fields-of-views located near cloud edges and/or within optically thin clouds, all data points within the 60-km radial distance of the detected point are removed. Validated with the AMSU-A derived LWP retrievals, about 50% of the clear sky data are successively identified and 99% of the cloudy radiances are successively removed for all NOAA-15 AMSU-A data on January 15, April 15, July 15, and October 15, 2002.

On July 8, 2014 meteorological satellite “Meteor-M” no. 2 with a module of temperature and humidity atmospheric sensing MTVZA-GY was launched on a circular sun-synchronous orbit. Microwave radiometer MTVZA-GY has 16 imaging channels at frequencies of 10.65, 18.7, 23.8, 31.5, 36.5, 42.0, 48.0, and 91.65 GHz and 13 sounder channels at frequencies in the ranges of 52–57 and 176–191 GHz and carries out conical scanning of the Earth at the angle of incidence 65°. Swath width is 1500 km. The brightness temperatures TBs were computed by numerical integration of microwave radiative transfer equation using the radiosonde and reanalysis atmospheric profiles as the input information. The TBs for the Amazon forest and the calm Ocean under clear sky served for the vicarious calibration of MTVZA channels. The long-term stability of MTVZA-GY operation was examined by the analysis of time series of TBs measured over the test areas around the Eastern Antarctic Dome C station, Greenland GEOSummit station and Amazon rainforest. TBs time series obtained by the GCOM-W1 AMSR2 radiometer for the same test areas served as a reference and were used for a comparison with MTVZA-GY time series. Examples are given of the global MTVZA-GY measurements.

This paper discusses the radiometric calibration of millimeter sounder radiometers, on polar orbiter satellites in the NASA Global Precipitation Mission (GPM) constellation; and presents radiometric bias results. Because the Tropical Rainfall Measurement Mission (TRMM) operated for over 17 years, it is important to combine the TRMM and the GPM precipitation datasets to produce a climate data record for global climate change studies. In the last decade of TRMM's operation, sounder radiometers were introduced into the TRMM constellation, which included: Advanced Microwave Sounding Unit-B sensors flown on NOAA weather satellites and the microwave humidity sounders sensors flown on NOAA and meteorological operational satellite program satellites. These sensors have provided an invaluable dataset of radiance measurements with full earth coverage, which has been used in precipitation measurements, weather prediction, and climate studies.

Two new constellations comprising 14 small satellites with microwave instruments onboard are proposed in this study. Properly arranged, the first constellation is capable of covering the entire globe at an hourly interval and the second constellation is more favorable for the tropical area. Compared to the current JPSS and MetOp satellite constellation, which has passive microwave sounding instruments ATMS or AMSU, a small satellite constellation is more cost effective, requires a shorter development cycle, and has a smaller launch-failure impact. For a designated microwave small satellite constellation, the brightness temperature distribution in space and time is simulated using the operational forecast fields as inputs to the Community Radiative Transfer Model (CRTM). It is demonstrated that the structural change of fast-evolving weather systems such as a middle-latitude cyclone can be well captured from small satellite brightness temperatures.

In this paper, the surface emissivity is retrieved over the Arctic sea ice/open seas using observations from the advanced microwave sounding unit window channels during the year 2009. The emissivity computation is performed using two contrasted surface assumptions: specular and Lambertian assumptions. The obtained sea ice surface emissivities are studied in this paper with a focus on the effect of the surface assumption. Some factors of variability of the obtained emissivities are analyzed: variability in space, in time, with the zenith angle, and with respect to the frequency. We show that the near-nadir surface emissivity and emissivity difference (obtained using two contrasted surface assumptions) could be used as an excellent proxy to detect ice/no ice regions. We also show that near-nadir sea ice emissivity at some selected frequencies and the combination of both high and low window frequencies could also be very useful to better characterize sea ice surface physical properties and provide additional information for existing sea ice classifications, as they bring relevant information about first year and multiyear sea ice properties and their seasonal evolution.

Atmospheric remote sensing techniques have become popular in the field of meteorology due to both the generation of spectral information of the atmosphere and the cover of wide regions in short periods of time. In this work, some relevant features about the Advance Technology Microwave Sounder (ATMS) are highlighted, in order to establish some basic criterias for assimilation and use of passive microwave data into algorithms to retrieve rain rate from spaceborne. In addition, an intercomparison with the Advance Microwave Unit Sensor (AMSU) is presented.

Polar lows are small intense low-pressure systems over polar seas associated with strong winds and heavy snowfall. Because of small scale and short lifetime, they are often missed by weather observation networks. As they can significantly affect ships and coastal communities and can have a large impact on lateral heat and moisture transport, better observation of polar lows with satellites is worthwhile. Here, we assess the detectability of polar lows using data from the microwave humidity sounders advanced microwave sounding unit-B (AMSU-B) and microwave humidity sounder (MHS) on the polar-orbiting satellites of NOAA. It turns out that cloud ice and frozen precipitation in polar lows cause strong scattering of upwelling atmospheric emission in the three water vapor channels around 183.31 GHz. As a result, most polar lows can be detected in brightness temperature differences of the water vapor channels as small areas of reversed sign. Out of 110 confirmed polar lows in the Nordic Seas, 85% could be clearly detected with AMSU-B. Looking for all polar-low-like signatures in AMSU-B/MHS data, we found about twice as many cases as the most thorough remote sensing inventory of polar lows published so far. The main reason is that in contrast to that inventory, we did not impose a minimum wind criterion which would need wind data from other sources. However, using the AMSU-B/MHS brightness temperature differences can be a quick tool to detect possible polar lows in near real time and this can equally well be applied to similar microwaves sounders operational on current and future satellites.

Asymmetric features of oceanic brightness temperature from spaceborne microwave imagers in high surface wind speed conditions were investigated with two kinds of collocated data. The first is simultaneous measurements of microwave brightness temperatures and surface wind vectors from the Advanced Microwave Scanning Radiometer (AMSR) and SeaWinds on Advanced Earth Observing Satellite II. The second is microwave brightness temperature observations (AMSR2 and the Special Sensor Microwave Imager Sounder) and surface wind vectors in the European Centre for Medium-Range Weather Forecasts numerical weather prediction model. Both collocated data sets showed that the vertical-polarized and the horizontal-polarized microwave brightness temperature have out-of-phase asymmetric features in terms of relative wind direction (RWD) at high surface wind speeds. Furthermore, different asymmetric features were found for the northern and Southern Hemispheres and for ascending and descending satellite orbits. Although similar asymmetric features can be found in other microwave imager studies, the causes of the asymmetry have not been fully investigated. To investigate the cause of the asymmetry, the observation frequency regarding air–sea temperature difference was examined in upwind, downwind, and crosswind cases. Two important factors contribute to the asymmetry. First, the observations from inclined polar orbit satellites provide different samplings on atmospheric stability in terms of the RWD. Second, the oceanic microwave brightness temperatures have negative correlations with atmospheric stability at high surface wind speeds. The out-of-phase asymmetry is closely related with atmospheric stability, and it appears under a high-surface-wind-speed condition.

In this paper, the oceanic radiometric calibration biases of the Advanced Microwave Scanning Radiometer-2 (AMSR2) onboard the Global Change Observation Mission-Water (GCOM-W1) are analyzed. The double difference ( DD ) approach is utilized to perform inter-sensor inter-calibration for AMSR2 with the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) as the reference radiometer. This technique utilizes radiative transfer model (RTM) simulations and near-simultaneous clear-sky ocean-only observed brightness temperatures from the two microwave radiometers to estimate and correct the radiometric biases of ocean scenes for AMSR2 on a channel by channel basis.

Here, we describe the characteristics of brightness temperature (Tb) measured by the Advanced Microwave Scanning Radiometer-2 (AMSR2) onboard the Global Change Observation Mission 1st-Water (GCOM-W1). This mission aims to achieve long-term global monitoring of Earth using two polar-orbiting satellite observation systems with three consecutive generations. GCOM-W1, the first satellite of the GCOM-W (Water) series, was launched successfully on May 18, 2012. AMSR2 is a single-mission instrument onboard the GCOM-W1 satellite. The basic characteristics of AMSR2 are similar to those of its predecessor, AMSR-E; this allows the continuation of AMSR-E observations but with several improvements, including a larger main reflector (2.0-m diameter), additional channels at C-band frequency, an improved calibration system, and increased reliability imparted by the addition of a redundant momentum wheel. Since July 3, 2012, the instrument has functioned properly and has accumulated a data set of Tb measurements. During the initial calibration and validation period, Tb values are being evaluated and characterized according to various methodologies, including intercalibration between similar microwave radiometers [e.g., the Tropical Rainfall Measuring Mission Microwave Imager (TMI)] based on radiative transfer computations. The intercalibration results for the ocean area as the cold end and the rainforest area the as warm end demonstrate that Tb measured by AMSR2 exhibits no apparent seasonal variation, with a maximum calibration difference of approximately 5 K compared to TMI and AMSR-E. This calibration difference appears to depend on the Tb of the observed object.

In this paper, the Backus–Gilbert (B–G) method was used for the conversion from Advanced Technology Microwave Sounder (ATMS) FOVs to AMSU-A FOVs. This method provides not only an optimal combination of measurements within a specified region but also a quantitative measure of the tradeoff between resolution and noise. Based on a subpixel microwave antenna temperature simulation technique, ATMS observations at a specified FOV size with 1.1 $^{\circ}$ sampling interval are simulated. Errors of remapping results were quantified by using simulated data sets and real AMSU observations. It is shown that the biases and/or standard deviations of brightness temperatures are significantly reduced by using the B–G generated remapping coefficients. For K/Ka bands, a resolution enhancement by the remap of ATMS observations introduces about 0.6 K increase in noise. For other bands, the channel sensitivity was improved for the remapped data.

In this letter, we study the performance of a K-band ground-based hyperspectral microwave radiometer for the observation of atmospheric water vapor. First, a prototype of a K-band ground-based hyperspectral microwave radiometer for atmospheric sounding is proposed. This microwave radiometer is able to split the 18–26-GHz signal into 80 hyperspectral channels with identical bandwidth. Simulation studies, including the retrieval performance of water vapor and the vertical resolution of observation compared with the five-humidity-channel radiometer TP/WVP-3000 under the same conditions, are presented to assess the capability of the prototype. Simulation results show that the vertical resolution of this prototype is better than that of TP/WVP-3000 at a higher altitude, and the RMS water vapor error improves by about 10% at an altitude of 0–6 km. Moreover, by simulation, we analyze the impact of the radiometer channel number on the Shannon information gain and the RMS water vapor error of the hyperspectral microwave radiometer. At an altitude of 1.5–6 km, more information can be obtained by increasing the number of microwave spectrum channels. For water vapor profiling, the improvement of the retrieval RMS error from 10 to 800 channels at a higher altitude exceeds about 5%–10%.

The synergistic use of microwave and hyperspectral infrared sounding observations gives rise to a rich array of signal processing challenges. Of particular interest are the following elements which are combined for the first time in the retrieval technique presented here: (1) radiance noise filtering and redundancy removal (compression) using principal components transforms and canonical correlations, (2) data fusion (infrared plus microwave at possibly different spatial and spectral resolutions) and stochastic cloud clearing (SCC), and (3) geophysical product retrieval from spectral radiance measurements using neural networks. In this paper, we describe the algorithm and demonstrate performance using the Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU). We show that performance is improved by approximately 25%-50% using the neural network method relative to other common techniques. Furthermore, we quantify the improvement in the vertical resolution of the retrieved products.

Microwave (MW) satellite data are widely used as input in numerical weather prediction models and also in other applications such as climate monitoring and re-analysis. MW satellite data are prone to different problems, including geolocation errors. These data do not have a fine spatial resolution like visible and infrared data; therefore, the accuracy of their geolocation cannot be easily determined using the normal methods such as superimposing coastlines on the satellite images. Currently, no geolocation correction is performed on data from MW instruments aboard the satellites in the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellite program. However, geolocation error can be a significant source of bias in the satellite measurements. In this paper, we investigated and corrected the geolocation errors of the observations from the Advanced Microwave Sounding Unit (AMSU)-A aboard NOAA-15 to NOAA-19, AMSU-B aboard NOAA-15 to NOAA-17, and Microwave Humidity Sounder (MHS) aboard NOAA-18 and NOAA-19. We used the difference between ascending and descending observations along the coastlines to quantify the geolocation errors in terms of the satellite attitudes (Euler angles), i.e., pitch, roll, and yaw. Then, new geographical coordinates and scan/local zenith angles were calculated using new attitudes. The results show that NOAA-15 AMSU-A2 instrument has a mounting error of about 1.2 ° cross-track, and -0.5° along-track, NOAA-16 AMSU-A1 and -A2 instruments have a mounting error of about -0.5° along-track, and NOAA-18 AMSU-A2 instrument has a mounting error of more than -1° along-track.

Radiances measured by satellite radiometers are often subject to biases due to limitations in their radiometric calibration. In support of the Global Space-based Inter-Calibration System project, to improve the quality of calibrated radiances from atmospheric sounders and imaging radiometers, an activity is underway to compare routinely measured radiances with those simulated from operational global numerical weather prediction (NWP) fields. This paper describes the results obtained from the first three years of these comparisons. Data from the High-resolution Infrared Radiation Sounder, Spinning Enhanced Visible and Infrared Imager, Advanced Along-Track Scanning Radiometer, Advanced Microwave Sounding Unit, and Microwave Humidity Sounder radiometers, together with the Atmospheric Infrared Sounder, a spectrometer, and the Infrared Atmospheric Sounding Interferometer, an interferometer, were included in the analysis. Changes in mean biases and their standard deviations were used to investigate the temporal stability of the bias and radiometric noise of the instruments. A double difference technique can be employed to remove the effect of changes or deficiencies in the NWP model which can contribute to the biases. The variation of the biases with other variables is also investigated, such as scene temperature, scan angle, location, and time of day. Many of the instruments were shown to be stable in time, with a few exceptions, but measurements from the same instrument on different platforms are often biased with respect to each other. The limitations of the polar simultaneous nadir overpasses often used to monitor biases between polar-orbiting sensors are shown with these results due to the apparent strong dependence of some radiance biases on scene temperature.

More than one decade of observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard the polar-orbiting satellites NOAA-15 to NOAA-19 and European Meteorological Operational satellite program-A (MetOp-A) provide global information on atmospheric temperature profile, water vapor, cloud, precipitation, etc. However, a pronounced asymmetric cross-scan bias of the AMSU-A window channels was discovered, and it severely impacted water cycle product generation. Several approaches, including vicarious cold and hot reference calibration techniques, are applied to characterize the cross-scan bias. The bias pattern appears to be stable through several years of data examined from the same satellite but is quite different among those onboard the different NOAA (NOAA-15, NOAA-16, NOAA-17, NOAA-18, and NOAA-19) and EUMETSAT (MetOp-A) satellites. The scan bias may be caused by sensor polarization misalignment or cross-polarization, even after the radiance/brightness temperature data have been geocorrected with regard to geolocation and view angles. Based upon the characterization information, two-point and three-point correction approaches are proposed; both approaches provide promising results for AMSU-A window channels at brightness temperature level and product level and outperform the current operational correction approach, which is essentially a one-point correction. This serves as the first step toward a more stable fundamental and thematic climate data record to be used in hydrological and meteorological applications.

MicroWave Humidity Sounder (MWHS) onboard both Fengyun-3A (FY-3A) and FY-3B satellites have three channels (channels 3-5) near the 183-GHz water-vapor absorption line. These channel frequencies are also used in other instruments such as Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (MHS) onboard MetOp and NOAA satellites. Both MWHS and MHS are cross-track scanners. In this paper, a comparison between the simulated brightness temperatures with MWHS measurements clearly shows that MWHS observations from the three sounding channels contain a scan-angle-dependent cohesive noise along the instrument scanline. This noise does not cancel out when a large amount of data over a sufficiently long period of time is averaged, which eliminates the possibility of such a noise to arise from the natural variability of the atmosphere and the surface. The noises are around 0.3, 0.2, and 0.2 K for channels 3-5, respectively. A principle component analysis is used for the characterization of this cohesive noise using one-month FY-3B MWHS data. It is shown that the MWHS cohesive noise is primarily contained in the first principal component (PC) mode, which mainly describes a scan-angle-dependent brightness temperature variation, i.e., a unique feature of the cross-tracking instrument. The first PC accounts for more than 99.91 % total variance in the three MWHS sounding channels. A five-point smoother is then applied to the first PC, which effectively removes such a data noise in the MWHS data. The reconstruction of the MWHS radiance spectra using the noise-filtered first PC component is of good quality. The scan-angle-dependent bias from the reconstructed MWHS data becomes more uniform and is consistent with the NOAA-18 MHS data.

Presented within this paper is an analysis of data from five flights of the Facility for Airborne Atmospheric Measurement BAe146 during the February 2008 Cold Land Processes II campaign over the North Slope of Alaska. Snow pits were dug under the aircraft tracks which provided information about snow pack stratigraphy and grain type, density, and temperature profiles. Lambertian emissivity retrievals were carried out yielding an emissivity and effective temperature time series for each of the five flights discussed. These emissivities were used as the truth for the validation of the Microwave Emission Model of Layered Snowpacks (MEMLS) appropriate for use at Microwave Humidity Sounder and Advanced Microwave Sounding Unit B frequencies. As there are uncertainties in the model input parameters which were determined from the snow pit measurements, an efficient global optimization routine was used to determine if MEMLS could adequately simulate the observed emissivities within the uncertainties of the input parameters. In order to reduce model errors, various optimization experiments were carried out. The most successful of these were obtained in the following two cases: 1) A parameterization of surface roughness was introduced, and 2) a limit on the scattering coefficient at high frequency was introduced. In both of these cases, MEMLS is able to reproduce the emissivity spectra over the sites studied within the uncertainties in the measurements.

Active and passive microwave remote sensing estimates of surface precipitation based on signals from hydrometeors aloft require correction for evaporated precipitation that would otherwise reach the ground. This paper develops and compares two near-surface evaporation correction methods using two years of data from 509 globally distributed rain gauges and three passive millimeter-wave Advanced Microwave Sounding Units (AMSUs) aboard National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-15, NOAA-16, and NOAA-18). The first type of correction is a scale factor that minimizes the bias between the means of annual AMSU and rain gauge precipitation accumulations (in millimeters per year) for each of 12 infrared-based surface classifications. The scale factor for the second correction method is computed using a neural network trained using both surface classification and annual average relative humidity pro files. AMSU surface precipitation retrievals using both methods were compared to the annual accumulations for 509 rain gauges uncorrected for wind effects, where different data were used for training and accuracy evaluation. The rms annual accumulation retrieval errors for AMSU using surface classification and relative humidity corrections were 236 and 222 mm/y, respectively, compared to 190 mm/y for corresponding Global Precipitation Climatology Project data, which utilizes more satellite sensors and over 6500 rain gauges.

A comprehensive system that is used to invert the geophysical products from microwave measurements has recently been developed. This system, known as the Microwave Integrated Retrieval System (MiRS), ensures that the final solution is consistent with the measurements and, when used as input to the forward operator, fits them to within the instrument noise levels. In the presence of precipitation, this variational algorithm retrieves a set of hydrometeor products consisting of cloud liquid water, ice water, and rain water content profiles. This paper presents the development and assessment of the MiRS rainfall rate that is derived based on a predetermined relationship of the rainfall with these hydrometeor products. Since this relationship relies on the geophysical products retrieved by the MiRS as inputs and not on sensor-dependent parameters, the technique is suitable for all microwave sensors to which the MiRS is applied. This precipitation technique has been designed to facilitate its transition from research to operations when applied to current and future satellite-based sensors. Currently, the MiRS rainfall rate technique has been implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) for the NOAA-18, NOAA-19, Metop-A Advanced Microwave Sounding Unit, and Microwave Humidity Sensor, as well as for the Defense Meteorological Satellite Program (DMSP)-F16 and DMSP-F18 Special Sensor Microwave Imager/Sounder microwave satellite sensors. For the validation of the MiRS rainfall rate technique, extensive comparisons with state-of-the-art precipitation products derived from rain gauge, ground-based radar, and satellite-based microwave observations are presented for different regions and seasons, and over land and ocean. The MiRS rainfall rate technique is shown to estimate precipitation, with a skill comparable to other satellite-based microwave precipitation algorithms, including the MSPPS, 3B40RT, and MWCOMB, while showing no discontinuities at coasts. This is a relevant result, considering that the MiRS is a system not merely designed to retrieve the rainfall rate but to consistently estimate a comprehensive set of atmospheric and surface parameters from microwave measurements.

An objective analysis (OA) method is implemented to compute the water vapor path delay (PD) correction of the altimeter range using total precipitable water measurements from scanning microwave radiometers (Advanced Microwave Sounding Unit A, Advanced Microwave Scanning Radiometer-Earth Observing System, Tropical Rain Measuring Mission Microwave Imager, and Special Sensor Microwave Imager). The European Centre for Medium Range Weather Forecasts (ECMWF) model-derived water vapor PD correction given in the altimeter products is used as the first-guess field. The calculation of the statistical variables required by the OA is presented: These include the variance and correlation function of the radiometer observations minus its first guess, as well as the observation error variance. The performance of the OA-derived water vapor PD correction is assessed, using four months of Jason-1 altimeter data. It is shown that the OA-derived correction is more accurate than the ECMWF-derived correction but remains less accurate than the one derived from the Jason microwave radiometer.

A microwave land emissivity library has been developed from the Advanced Microwave Sounding Unit (AMSU) data for improving satellite data assimilation. Over the desert, surface emissivity is classified according to soil type into several spectra. For sand, loamy sand, and sandy loam, which contain some large mineral particles, the emissivity spectra generally decrease with frequency. For other desert types whose compositions are dominated by mineral particles smaller than a few hundred micrometers, the emissivity values are almost constant or slightly increasing with frequency. These emissivity features are consistent with those from the land emissivity data set developed at Meteo-France. Moreover, both the emissivity library and the Meteo-France data set are applied to the assimilation of the AMSU-A data in the National Centers for Environmental Prediction Global Forecast System (GFS). In comparison with the microwave land emissivity model previously developed by Weng , both the emissivity library and the Meteo-France data set improve the utilization of the AMSU-A data in the GFS. The increased use of the AMSU-A data through the emissivity library or the data set results in positive impacts on the global medium-range forecasts over either the Southern or Northern Hemispheres.

This paper describes the Atmospheric Infrared Sounder (AIRS) Science Team Version 5 retrieval algorithm in terms of its three most significant improvements over the methodology used in the AIRS Science Team Version 4 retrieval algorithm: the use of AIRS clear-column radiances in the entire 4.3-μm CO2 absorption band in the retrieval of temperature profiles T(p) during both day and night, with tropospheric sound ing of 15-μm CO2 observations now being used primarily in the generation of clear-column radiances Ri for all channels; development of a new methodology to provide accurate case-by-case error estimates for retrieved geophysical parameters and for channel-by-channel clear column radiances and their use in a new approach for quality control; and an approach to provide AIRS soundings in partially cloudy conditions that does not require use of any microwave data. This new AIRS-only sounding methodology, referred to as AIRS Version 5 AO, was developed as a backup to AIRS Version 5 should the Advanced Microwave Sounding Unit (AMSU)-A instrument fail. Results are shown that compare the relative performance of the AIRS Version 4, Version 5, and Version 5 AO. Results using Version 5 retrievals in conjunction with different quality control thresholds are also shown for a recent period to demonstrate that empirical coefficients continue to be applicable in later time periods. The Goddard Data and Information Services Center (DISC) is now generating and distributing products derived using the AIRS Science Team Version 5 retrieval algorithm. This paper describes the quality control flags contained in the DISC AIRS/AMSU retrieval products and their intended use for scientific purposes.

Passive microwave sea-ice retrieval algorithms are typically tuned to brightness temperature measurements with simple treatments of weather effects. The new technique presented is a two-step algorithm that variationally retrieves surface emissivities from microwave remote sensing observations, followed by the retrieval of sea-ice concentration from surface emissivities. Surface emissivity spectra are interpreted for determining sea-ice fraction by comparison with a catalog of sea-ice emissivities to find the closest match. This catalog was computed off-line from known ocean, first-year, and multiyear sea-ice reference emissivities for a range of fractions. The technique was adjusted for application to the Advanced Microwave Sounding Unit (AMSU)/Microwave Humidity Sensor observations, and its performance was compared to the National Oceanic and Atmospheric Administration (NOAA)'s AMSU heritage sea-ice algorithm and to NOAA's operational Interactive Multi-sensor Snow and Ice Mapping System taken as ground truth. Assessment results indicate a performance that is superior to the heritage algorithm particularly over multiyear ice and during the warm season.

This study introduces a variational approach to retrieve total precipitable water (TPW) over all surface backgrounds including ocean, land, snow, sea-ice, and coastal areas, from microwave sensors. The product has been used routinely by forecasters since its recent operational implementation. The emissivity is accounted for by including its spectrum within the retrieved state vector, which allows for a pixel-to-pixel variation of the emissivity, a factor usually preventing the TPW retrieval over land. The algorithm, implemented operationally at the National Atmospheric and Oceanic Administration (NOAA), is called the Microwave Integrated Retrieval System (MiRS). Its main characteristic, besides its applicability over all surfaces, is its validity under all weather conditions. With a generic design, the algorithm is being applied to the following microwave sensors: (1) AMSU and MHS onboard NOAA-18; (2) NOAA-19 and Metop-A; as well as (3) SSMI/S onboard DMSP-F16 platform. The assessment of the MiRS performances is done by undertaking extensive comparisons to the National Center for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses, to a network of radiosondes and to existing well-established algorithms over ocean, encompassing a wide variety of meteorological situations. The performance of MiRS TPW is shown to depend on the sensor, the reference data source as well as on the surface background considered. It is shown to behave quite well over all surfaces and in all weather conditions, except when there is rain. Although this study focuses on the retrieval of TPW with an emphasis on non-oceanic surfaces, the underlying application of this study is the potential improvement in the variational data assimilation of Numerical Weather Prediction (NWP) models. Indeed, the same dynamic approach could be employed in order to assimilate more surface-sensitive microwave channels, over a multitude of surfaces.

This letter evaluates the ability of the U.S. National Polar-orbiting Operational Environmental Satellite System 22-channel Advanced Technology Microwave Sounder (ATMS) to retrieve surface precipitation rates (millimeters per hour); water path estimates for rain, snow, and graupel (millimeters); and peak vertical wind (convective strength, meters per second). Simulated retrieval accuracies for ATMS were compared to those for its predecessor the Advanced Microwave Sounding Unit (AMSU), both of which sample the 23- to 190-GHz spectrum. ATMS retrieves all precipitation parameters up to ~ 25% more accurately than AMSU except for cloud ice and snow water paths, which are comparable. Image sharpening of the ATMS Nyquist-sampled observations below 90 GHz further improves the recovery of small features but amplifies noise so that the benefits are restricted primarily to finely structured convective systems.

We present an approach to retrieve daily minimum and maximum 2-m height air temperatures from 18.7, and 23.8 GHz H and V polarized brightness temperature from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) during the snow free season. The approach accounts, with minimal ancillary data, for vertically integrated atmospheric water vapor, and variable surface emissivity due to open water and vegetation. Retrieved temperatures were evaluated using Northern Hemisphere weather stations and independent satellite-based air temperatures from the Atmosphere Infrared Sounder and Advanced Microwave Sounding Unit (AIRS/AMSU; hereafter AIRS) sensors on Aqua. The retrieved temperatures are within 1.0 - 3.5 K of surface weather station measurements for vegetated locations, but uncertainty can exceed 4 K for desert and sparsely vegetated regions, mainly due to site to site biases. The AIRS and AMSR-E temperature retrievals generally agree more closely with one another than with weather stations and are generally within 1.0-2.8 K over vegetated regions, but with less agreement ( > 4 K ) over desert and mountainous regions. Additional useful information produced by our approach includes open water fraction, vegetation optical depth and atmospheric water vapor. The results of this study provide inputs for land surface models and a new approach for monitoring of land surface air temperatures with well quantified accuracy and precision.

A new global precipitation retrieval algorithm for the millimeter-wave Advanced Microwave Sounding Unit is presented that also retrieves Arctic precipitation rates over surface snow and ice. This algorithm improves upon its predecessor by excluding some surface-sensitive channels and by reducing the number of principal components (PCs) used to represent those that remain. The training sets were also modified to better represent cold regions. The algorithm still incorporates conversion of brightness temperatures to nadir, spatial filtering to better detect pixels scattering near 54 GHz, PC filtering of surface effects, and use of separate neural networks trained with the fifth-generation National Center for Atmospheric Research/Penn State Mesoscale Model (MM5) for land and sea, where warm and cold ocean are now treated differently. The validity of the snowfall detections is supported by nearly coincident CloudSat radar observations, and the physics of the model is largely validated by the reasonable agreement in annual precipitation obtained for 231 globally distributed rain gauges, including many at latitudes where snowfall dominates. Observed annual global precipitation statistics are also presented to permit comparisons with other algorithms and sensors.

We develop an over-ocean rainfall retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) based on the Global Satellite Mapping of Precipitation (GSMaP) microwave radiometer algorithm. This algorithm combines an emission-based estimate from brightness temperature (Tb) at 23 GHz and a scattering-based estimate from Tb at 89 GHz, depending on a scattering index (SI) computed from Tb at both 89 and 150 GHz. Precipitation inhomogeneities are also taken into account. The GSMaP-retrieved rainfall from the AMSU (GSMaP_AMSU) is compared with the National Oceanic and Atmospheric Administration (NOAA) standard algorithm (NOAA_AMSU)-retrieved data using Tropical Rainfall Measuring Mission (TRMM) data as a reference. Rain rates retrieved by GSMaP_AMSU have better agreement with TRMM estimates over midlatitudes during winter. Better estimates over multitudes over winter are given by the use of Tb at 23 GHz in the GSMaP_AMSU algorithm. It was also shown that GSMaP_AMSU has higher rain detection than NOAA_AMSU.

This paper presents a new assortment of temperature-sounding channels for a proposed low-Earth-orbit polar Sun-synchronous satelliteborne millimeter-wave atmospheric sounder of the Indian Space Research Organization (ISRO). The newness owes its origin to the exploration of the millimeter-wave O2 absorption spectrum in quest of optimal off-resonance frequencies that impose fewer restrictions on channel bandwidth and temperature sensitivity and yet can sound up to 40 km in the atmosphere with a 4-km vertical resolution. This is ISRO's first leap toward millimeter-wave technology. The overall receiver-noise figure for the channels in the 5-mm band (50-60 GHz) has been pessimistically estimated at 5 dB which will severely degrade the system temperature sensitivity. Therefore, channel bandwidth is at premium. The purpose of this design is to limit the number of passbands and simplify the design of frequency-selective filters by choosing center frequencies that have sufficient interleaving and also provide a scope of allotting a reasonable bandwidth. The set of temperature-sounding channels in the current design have 15 channels in 17 passbands in contrast with 15 channels in 29 passbands of the operational Advanced Microwave Sounding Unit (AMSU)-A.

The Advanced Microwave Sounding Unit-A (AMSU-A) on the Meteorological Operations Platform A (METOP-A) Satellite was successfully launched in October 2006. The AMSU-A, which was provided by NOAA, is one of a new generation of total-power microwave radiometers which have been flown on the NOAA-15 to NOAA-18 Satellites since May 1998. A systematic postlaunch calibration and validation (cal/val) of the instrument performance was conducted with on-orbit data. A brief report of the postlaunch assessment of the instrument performance is presented in this paper. Scan-by-scan examination of the radiometric calibration counts is employed to confirm normal functioning of the instrument and to detect any anomalous events, such as lunar contamination (LC) in the space radiometric counts, which are detected, flagged, and corrected using an algorithm for detection and correction of LC in AMSU-A data. The correction algorithm provides a practical approach for scan-by-scan correction of the LC in AMSU-A data and improves the accuracy of operational calibration of the NOAA Level IB data. The long-term trends of the space and warm calibration counts, channel gains, and housekeeping temperature sensors are monitored. Temperature sensitivity (or NEDeltaT) values for individual channels are also examined. The monthly mean angular distributions of brightness temperatures from the METOP-A and NOAA-15, -16, and -18 over the tropical ocean region of 20deg S-20deg N are obtained and theoretically modeled to demonstrate that the ocean can be used as a cold reference calibration target. The establishment of a natural Earth calibration target is an important addition to the few tools available to date for cal/val of spaceborne microwave radiometers. The measured ocean brightness temperatures from the four satellites provide eight measurements across the diurnal time for a unique opportunity of studying the diurnal variation of ocean brightness temperatures which show a pattern of daytime cooling and nighttime warming.

A function of Advanced Microwave Sounding Unit-A (AMSU-A) measurements at 50.3 and 52.8 GHz is defined, which has the property of being sensitive to the angular distribution of surface-scattered atmospheric thermal emission but insensitive to the surface reflectivity, assuming reflectivity to be equal at the two frequencies. A scattering model consisting of a linear combination of specular reflection and Lambertian scattering is fitted to observed values of the function over Antarctica. The Lambertian fraction typically lies in the range of 0.7-1.0 with a mean of 0.84, and it is negatively correlated with polarization of the surface reflectivity, as determined from Special Sensor Microwave/Imager data. Dual-polarized 37-GHz measurements, combined with a separate measurement of the surface temperature, could be used as a predictor of the diffuseness of surface scattering when the atmospheric temperature profile is retrieved from measurements made by AMSU-A or a similar instrument, or when the radiances are assimilated for numerical weather prediction.

A method to retrieve the surface emissivity of sea ice at the window channels of the Advanced Microwave Sounding Unit (AMSU) radiometers in the polar region is presented. The instruments are on the new-generation satellites of the U.S. National Oceanic and Atmospheric Administration (NOAA-15, NOAA-16, and NOAA-17). The method assumes hypothetical surfaces with emissivities zero and one and simulates brightness temperatures at the top of the atmosphere using profiles of atmospheric parameters, e.g., from the European Centre for Medium-Range Weather Forecasts (ECMWF) model runs, as input for a radiative transfer model. The retrieval of surface emissivity is done by combining simulated brightness temperatures with the satellite-measured brightness temperature. The AMSU window channels differ in surface penetration depths and, thus, in the surface microphysical parameters that they depend on. Lowest layer air temperatures from ECMWF are used to infer temperatures of emitting layers at different frequencies of sea ice. A complete yearly cycle of monthly average emissivities in two selected regions (first- and multiyear ice) is giving insight into the variation of emissivities in various development stages of sea ice.

The polar regions are among those where the least information is available about the current and predicted states of surface and atmosphere. We present advances in a method to retrieve the total water vapor (TWV) of the polar atmosphere from data from spaceborne microwave radiometers such as the Advanced Microwave Sounding Unit B (AMSU-B) on the polar-orbiting satellites of the National Oceanic and Atmospheric Administration (NOAA), NOAA-15, -16, and -17. The starting point of the retrieval is a recently proposed algorithm that uses the three AMSU-B channels centered around the 183-GHz water vapor line and the window channel at 150 GHz, and that can retrieve the TWV with little dependence on the surface emissivity. This works up to TWV values of about 7 kg/m2. We extend the retrievable range toward higher TWV values by including the window channel at 89 GHz. However, now, the algorithm needs information on the surface emissivity, which we have extracted from emissivity measurements over sea ice and open water during the Surface Emissivities in Polar Regions-Polar Experiment campaign. The resulting algorithm can retrieve TWV up to about 15 kg/m2, with reduced accuracy as compared to the original algorithm. It now allows the monitoring of the TWV over the central Arctic sea ice and over Antarctica, and the surrounding sea ice during most of the year with a spatial resolution of about 50 km. Such TWV fields can show details which might be missed out by standard weather model analysis data.

A 1-D variational retrieval of surface emissivity is developed and applied for the Advanced Microwave Sounding Unit modules A and B, along with the High-resolution Infrared Radiation Sounder. This algorithm offers simultaneous retrieval of infrared and microwave emissivity and increases the separation of the emissivity and land surface temperature signals. The initial estimate of the emissivity for the surface-sensitive channels is made by a combination of physical and empirical microwave emissivity models and, in the infrared, by indexing laboratory measurements to vegetation databases. It is found that the initial estimates of emissivity for snow-free vegetated land areas are within 1% of the retrieved infrared values and, in the microwave, within 4% for all snow-free points and within 2% for the vast majority. The emissivity for snow-covered and sea-ice areas remains problematic, and further investigation is required. It is also shown that the average zenith angle dependence of the emissivity is less than 0.0024 for infrared wavelengths and less than 0.0055 for microwave frequencies if the viewing zenith angles greater than 40 are neglected.

The emission and scattering from desert surfaces are analyzed using simulations and measurements from the Special Sensor Microwave/Imager (SSM/I) and the Advanced Microwave Sounding Unit (AMSU) microwave satellite instruments. Deserts are virtually free of vegetation, so the satellite radiometers are able to observe the emissivities of different minerals, such as limestone and quartz. Moreover, since deserts contain little moisture, the thermal emission originates below the surface at a depth of many wavelengths. At high frequencies, where the penetration depth of radiation is smallest, the radiometric measurements display the large diurnal variation in surface temperature, which reaches its maximum at around 1 P.M. Conversely, at low frequencies, where the penetration depth is largest, the radiation measurements display the small diurnal variation of subsurface temperature, which reaches a minimum at around 6 A.M. In addition to these emission signals, sand particles also scatter microwave radiation. Volume scattering causes the measurements to decrease as the frequency increases; although compared to other scattering media (snow cover and precipitation), the larger absorption and fractional volume (i.e., solidity) of sand reduce the scattering. Although the scattering effect is small, SSM/I measurements between 19 and 85 GHz show that deserts scatter the upwelling microwave radiation in a manner similar to light precipitation, which makes it difficult to uniquely identify precipitation over arid regions. Interestingly, the higher frequency AMSU measurement at 150 GHz is nearly the same as at 89 GHz for deserts, whereas the 150-GHz measurement is much lower than at 89 GHz for precipitation. These different spectral features at high frequencies can provide a means of separating the scattering from desert surfaces from that of precipitation.

This paper develops a global precipitation rate retrieval algorithm for the advanced microwave sounding unit (AMSU), which observes 23-191 GHz. The algorithm was trained using a numerical weather prediction (NWP) model (MM5) for 106 globally distributed storms that predicted brightness temperatures consistent with those observed simultaneously by AMSU. Neural networks were trained to retrieve hydrometeor water-paths, peak vertical wind, and 15-min average surface precipitation rates for rain and snow at 15-km resolution at all viewing angles. Different estimators were trained for land and sea, where surfaces classed as snow or ice were generally excluded from this paper. Surface-sensitive channels were incorporated by using linear combinations [principal components (PCs)] of their brightness temperatures that were observed to be relatively insensitive to the surface, as determined by visual examination of global images of each brightness temperature spectrum PC. This paper also demonstrates that multiple scattering in high microwave albedo clouds may help explain the observed consistency for a global set of 122 storms between AMSU-observed 50-191-GHz brightness temperature distributions and corresponding distributions predicted using a cloud-resolving mesoscale NWP model (MM5) and a two-stream radiative transfer model that models icy hydrometeors as spheres with frequency-dependent densities. The AMSU/MM5 retrieval algorithm developed in Part I of this paper is evaluated in Part II on a separate paper.

This paper evaluates the performance of the global precipitation rate retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) that was described in Part I of this paper. AMSU is in polar orbit on several National Ocean and Atmospheric Administration (NOAA) operational weather satellites. Predicted rms retrieval errors based on a 15-km resolution 0.5-1.0-mm/h MM5 truth were 0.88, 0.83, 1.13, and 3.04 for stratiform, warm rain, ice-free rain, and convective rain, respectively, which were averaged over all view angles for land and sea up to 73deg latitude. For MM5 rates of 4-8 mm/h, these rms errors increased to 2.8, 3.4, 3.9, and 4.9 mm/h, respectively. The corresponding rms retrieval accuracies for MM5 hydrometeor water paths between 0.125 and 0.25 mm for rainwater, snow, and graupel were 0.19, 0.10, and 0.22 mm, respectively. The rms retrieval accuracy for the 0.125-0.25-m/s peak vertical wind was 0.08 m/s. Biases are small for cumulative precipitation estimates, although an upward correction factor of 1.37 is derived for convective precipitation rate probability distributions. Differences between these retrievals and those from the conically scanned Advanced Microwave Scanning Radiometer for the Earth Observing System instrument and an alternate NOAA AMSU algorithm are also characterized.

Only instruments on geostationary or comparable platforms can view global precipitation at the ~15-min interval that is necessary to monitor rapidly evolving convective events. This paper compares the abilities of 11 alternative passive microwave sensors to retrieve surface precipitation rates and hydrometeor water paths. Five instruments observe selected frequencies from 116 to 429 GHz with a filled-aperture antenna, and six instruments observe from 52 to 191 GHz with a U-shaped aperture synthesis array. The analysis is based on neural network retrieval methods and 122 global MM5-simulated storms that are generally consistent with the simultaneous Advanced Microwave Sounding Unit observations. Several instruments show considerable promise in retrieving hydrometeor water paths and 15-min average precipitation rates ~1-100 mm/h with spatial resolutions that vary from ~15 to ~50 km. This space/time resolution is potentially adequate to support assimilation of precipitation information into cloud-resolving numerical weather prediction models.

This paper presents the results of a simple information content study between the Advanced Microwave Sounding Unit/Microwave Humidity Sounder (AMSU/MHS) and the Advanced Technology Microwave Sounder (ATMS). When a single field of view is considered for both instruments, the AMSU/MHS generally outperforms the ATMS for temperature and moisture information due to its better noise performance. However, when footprint matching is employed to use oversampled ATMS observations, the ATMS consistently shows improvement in temperature and moisture information over the AMSU/MHS.

The National Oceanic and Atmospheric Administration (NOAA)-18 Polar-orbiting Operational Environmental Satellite (POES) was successfully launched with the fourth Advanced Microwave Sounding Unit-A onboard in May 2005. After launch, a systematic post launch calibration and validation of the instrument performance was conducted with on-orbit data. A brief report of the initial assessment of the instrument performance is presented in this paper. Scan-by-scan examination of the radiometric calibration counts is employed to confirm normal functioning of the instrument and to detect any anomalous events, such as lunar contamination in the space radiometric counts, which are accurately detected, flagged, and corrected using a physical model of lunar surface temperature and antenna patterns derived from on-orbit data. The long-term trends of the space and warm calibration counts, channel gains, and housekeeping temperature sensors are monitored. Temperature sensitivity (or the noise-equivalent ) values for individual channels are also monitored since launch. The long-term temporal trends of the monthly averages and angular distributions of brightness temperature measurements from the NOAA-16 and -18 over the Amazon rain forest region are obtained and compared to demonstrate that the Amazon rain forest can be used as a hot reference calibration target. The establishment of a land calibration target is an important addition to the few tools available to date for calibration and validation of spaceborne microwave radiometers.

Brightness temperatures over the Amazon rainforest are obtained from the Advanced Microwave Sounding Units (AMSU-A and AMSU-B) instruments onboard three NOAA satellites (NOAA-15, -16, and -17, respectively) for the months of July, August, and October, 2002. The three AMSU-A instruments provided six daily measurements, separated by 2.5-5.5 h of the diurnal time intervals, over the Amazon rainforest region, and these measurements offer a unique opportunity for investigation of the diurnal variation of the brightness temperatures over the Amazon rainforests. The angular distributions of brightness temperatures over the Amazon rainforest are very stable and can be simulated with a radiative transfer model, which consists of an atmospheric radiative component and a rainforest-canopy model that treats the rainforest as a uniform layer with an effective canopy temperature. The simulated results agree well with the observations. The diurnal variation of brightness temperatures over the Amazon rainforest is simulated with a Fourier-series model. It shows that a second order of Fourier series can reproduce the observed pattern of diurnal variation of the brightness temperatures at zenith angles of 0deg, 28.7deg, and 58.1deg, respectively. In a practical application, the coefficients of Fourier-series expansion can be used to generate the brightness temperatures as a function of diurnal hours. These results can be applied to postlaunch calibration of satellite-borne microwave radiometer with different equator crossing time. In addition, the results presented in this paper indicate that the Amazon rainforest can be used as a hot calibration reference target. The availability of a land calibration target is important for calibration and validation of spaceborne microwave radiometers

Snow fall and snow accumulation are key climate parameters due to the snow's high albedo, its thermal insulation, and its importance to the global water cycle. Satellite passive microwave radiometers currently provide the only means for the retrieval of snow depth and/or snow water equivalent (SWE) over land as well as over sea ice from space. All algorithms make use of the frequency-dependent amount of scattering of snow over a high-emissivity surface. Specifically, the difference between 37- and 19-GHz brightness temperatures is used to determine the depth of the snow or the SWE. With the availability of the Advanced Microwave Scanning Radiometer (AMSR-E) on the National Aeronautics and Space Administration's Earth Observing System Aqua satellite (launched in May 2002), a wider range of frequencies can be utilized. In this study we investigate, using model simulations, how snow depth retrievals are affected by the evolution of the physical properties of the snow (mainly grain size growth and densification), how they are affected by variations in atmospheric conditions and, finally, how the additional channels may help to reduce errors in passive microwave snow retrievals. The sensitivity of snow depth retrievals to atmospheric water vapor is confirmed through the comparison with precipitable water retrievals from the National Oceanic and Atmospheric Administration's Advanced Microwave Sounding Unit (AMSU-B). The results suggest that a combination of the 10-, 19-, 37-, and 89-GHz channels may significantly improve retrieval accuracy. Additionally, the development of a multisensor algorithm utilizing AMSR-E and AMSU-B data may help to obtain weather-corrected snow retrievals.

A novel statistical method for the retrieval of atmospheric temperature and moisture profiles has been developed and evaluated with simulated clear-air and observed partially cloudy sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU). The algorithm is implemented in two stages. First, a projected principal components (PPC) transform is used to reduce the dimensionality of and optimally extract geophysical profile information from the cloud-cleared infrared radiance data. Second, a multilayer feedforward neural network (NN) is used to estimate the desired geophysical parameters from the PPCs. For the first time, NN temperature and moisture retrievals are presented using actual microwave and hyperspectral infrared observations of cloudy atmospheres, over both ocean and land (with variable terrain elevation), and at all sensor scan angles. The performance of the NN retrieval method (henceforth referred to as the PPC/NN method) was evaluated using global Earth Observing System Aqua orbits colocated with European Center for Medium-range Weather Forecasting fields for seven days throughout 2002 and 2003. Over 350,000 partially cloudy footprints were used in the study, and retrieval performance was compared with the AIRS Science Team Level-2 retrieval algorithm (version 3). Performance compares favorably with that obtained with simulated clear-air observations from the NOAA88b radiosonde set of approximately 7500 profiles. The PPC/NN method requires significantly less computation than traditional variational retrieval methods, while achieving comparable performance.

In this work, two microwave emissivity parameterizations are proposed to estimate the land emissivity at Advanced Microwave Sounding Units (AMSU) frequencies and scanning conditions in order to help processing AMSU measurements over land surfaces. Both parameterizations are derived from previously calculated land emissivities directly from satellite observations and take into account different surface types from bare soil to areas with high vegetation density. The first parameterization uses best-fit functions derived from February 2000 observations whereas the second parameterization is based on the first one with the addition of a mean nadir emissivity map at 23.8 GHz to allow a more precise surface description. The emissivity parameterizations have been evaluated by comparing emissivity and Tb simulations to target emissivity and Tb observations.

A sensitivity study was performed with the commonly used millimeter-wave propagation model (MPM) to assess the impact of prescribed uncertainty in the temperature dependence of the line-coupling factors used to model the oxygen line shape. In some cases, apparent nonphysical behaviors were noticed and their impact on microwave channels was found to be significant and more importantly, nonremovable by simple calibration or bias removal. The Advanced Microwave Sounding Unit configuration in particular was tested because of its prime importance in current data assimilation. This impact, which reached a maximum of a few Kelvin in some channels, is modulated nonlinearly by the amount of water vapor present in the atmosphere, with maximum impact in dry air situations, which makes it dependent on the brightness temperature itself. These errors directly impact the error budget of any physically based geophysical retrieval. The line shape of these O/sub 2/ lines has impacts across the microwave spectrum, through the wing effects. This includes channels near 23, 36, and 85 GHz, commonly used in operational radiometry.

With the launch of the NOAA-15 satellite in May 1998, a new generation of passive microwave sounders was initiated. The Advanced Microwave Sounding Unit (AMSU), with 20 channels spanning the frequency range from 23-183 GHz, offers enhanced temperature and moisture sounding capability well beyond its predecessor, the Microwave Sounding Unit (MSU). In addition, by utilizing a number of window channels on the AMSU, the National Oceanic and Atmospheric Administration (NOAA) expanded the capability of the AMSU beyond this original purpose and developed a new suite of products that are generated through the Microwave Surface and Precipitation Products System (MSPPS). This includes precipitation rate, total precipitable water, land surface emissivity, and snow cover. Details on the current status of the retrieval algorithms (as of September 2004) are presented. These products are complimentary to similar products obtained from the Defense Meteorological Satellite Program Special Sensor Microwave/Imager (SSMI) and the Earth Observing Aqua Advanced Microwave Scanning Radiometer (AMSR-E). Due to the close orbital equatorial crossing time between NOAA-16 and the Aqua satellites, comparisons between several of the MSPPS products are made with AMSR-E. Finally, several application examples are presented that demonstrate their importance to weather forecasting and analysis, and climate monitoring.

The measurements from satellite microwave imaging and sounding channels are simultaneously utilized through a one-dimensional (1-D) variation method (1D-var) to retrieve the profiles of atmospheric temperature, water vapor and cloud water. Since the radiative transfer model in this 1D-var procedure includes scattering and emission from the earth's atmosphere, the retrieval can perform well under all weather conditions. The iterative procedure is optimized to minimize computational demands and to achieve better accuracy. At first, the profiles of temperature, water vapor, and cloud liquid water are derived using only the AMSU-A measurements at frequencies less than 60 GHz. The second step is to retrieve rain and ice water using the AMSU-B measurements at 89 and 150 GHz. Finally, all AMSU-A/B sounding channels at 50-60 and 183 GHz are utilized to further refine the profiles of temperature and water vapor while the profiles of cloud, rain, and ice water contents are constrained to those previously derived. It is shown that the radiative transfer model including multiple scattering from clouds and precipitation can significantly improve the accuracy for retrieving temperature, moisture and cloud water. In hurricane conditions, an emission-based radiative transfer model tends to produce unrealistic temperature anomalies throughout the atmosphere. With a scattering-based radiative transfer model, the derived temperature profiles agree well with those observed from aircraft dropsondes.