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A new data product calculates snowfall rates from weather data beamed directly from several satellites, helping meteorologists provide fast, accurate weather reports and forecasts.

The new Version 2.3 of the Global Precipitation Climatology Project (GPCP) Monthly analysis is described in terms of changes made to improve the homogeneity of the product, especially after 2002. These changes include corrections to cross-calibration of satellite data inputs and updates to the gauge analysis. Over-ocean changes starting in 2003 resulted in an overall precipitation increase of 1.8% after 2009. Updating the gauge analysis to its final, high-quality version increases the global land total by 1.8% for the post-2002 period. These changes correct a small, incorrect dip in the estimated global precipitation over the last decade given by the earlier Version 2.2. The GPCP analysis is also used to describe global precipitation in 2017. The general La Nina pattern for 2017 is noted and the evolution from the early 2016 El Nino pattern is described. The 2017 global value is one of the highest for the 1979–2017 period, exceeded only by 2016 and 1998 (both El Nino years), and reinforces the small positive trend. Results for 2017 also reinforce significant trends in precipitation intensity (on a monthly scale) in the tropics. These results for 2017 indicate the value of the GPCP analysis, in addition to research, for climate monitoring.

The Goddard profiling algorithm has evolved from a pseudoparametric algorithm used in the current TRMM operational product (GPROF 2010) to a fully parametric approach used operationally in the GPM era (GPROF 2014). The fully parametric approach uses a Bayesian inversion for all surface types. The algorithm thus abandons rainfall screening procedures and instead uses the full brightness temperature vector to obtain the most likely precipitation state. This paper offers a complete description of the GPROF 2010 and GPROF 2014 algorithms and assesses the sensitivity of the algorithm to assumptions related to channel uncertainty as well as ancillary data. Uncertainties in precipitation are generally less than 1%–2% for realistic assumptions in channel uncertainties. Consistency among different radiometers is extremely good over oceans. Consistency over land is also good if the diurnal cycle is accounted for by sampling GMI product only at the time of day that different sensors operate. While accounting for only a modest amount of the total precipitation, snow-covered surfaces exhibit differences of up to 25% between sensors traceable to the availability of high-frequency (166 and 183 GHz) channels. In general, comparisons against early versions of GPM’s Ku-band radar precipitation estimates are fairly consistent but absolute differences will be more carefully evaluated once GPROF 2014 is upgraded to use the full GPM-combined radar–radiometer product for its a priori database. The combined algorithm represents a physically constructed database that is consistent with both the GPM radars and the GMI observations, and thus it is the ideal basis for a Bayesian approach that can be extended to an arbitrary passive microwave sensor.

A method is presented to calculate layer-averaged tropospheric humidity (LAH) from the observations of the Advanced Technology Microwave Sounder (ATMS) water vapor channels. The method is based on a linear relation between the satellite brightness temperatures (Tb) and natural logarithm of Jacobian weighted humidity. The empirical coefficients of this linear relation were calculated using different data sets, as well as a fast and a line-by-line radiative transfer (RT) model. It was found that the coefficients do not significantly depend on the data set or the RT model. This Tb to the LAH transformation method can be applied to either original or limb-corrected ATMS Tb's. The method was validated using both simulated and observed ATMS Tb's. The systematic difference between the estimated and calculated LAH values was less than 10% in most cases. We also tested the transformation method using a fixed Jacobian for each channel. The bias generally increases when fixed Jacobians are used, but there is still a satisfactory agreement between estimated and calculated LAH values. In addition, the spatial distribution of the bias was investigated using the European Center for Medium-Range Weather Forecasting (ECMWF) Interim Reanalysis (ERA-interim) and collocated ATMS observations. The bias did not indicate any significant regional dependence when actual Jacobians were used, but in the case of fixed Jacobians, the bias generally increased from middle latitude toward the poles.

Hurricane Sandy – a tropical cyclone that transitioned into an extratropical cyclone near the time of landfall along the east coast of the United States – caused historic damage in many regions which rarely receive such a direct hit from a storm of this magnitude, including many of the large metropolitan areas along the U.S. eastern seaboard. Specifically, Sandy generated record low-pressure, a large wind field with corresponding storm surge and copious amounts of precipitation in some areas, including record snowfall in mountainous regions. Sandy presented several forecast challenges to the National Oceanic and Atmospheric Administration's (NOAA) National Weather Service (NWS). Satellites played an integral role in the analysis and forecast of Sandy's track and intensity. The NOAA National Hurricane Center, Ocean Prediction Center, and Weather Prediction Center all relied on information from satellites to make critical warning decisions using various satellite products that assist with diagnosing tropical cyclone intensity, surface winds over the ocean, and heavy precipitation. All of the skillful global forecast models used satellite data for initiation to better forecast the track and intensity of Sandy. As part of the Geostationary Operational Environmental Satellite — R-series (GOES-R) and Joint Polar Satellite System (JPSS) Proving Ground activities, new satellite products were available to forecasters at these national centers in experimental form to assist with observing this unique, high impact event. This paper will demonstrate how the current satellite products assisted NOAA forecasters during Sandy and introduce some new satellite products that could be used to analyze and predict future high impact weather systems.

This paper evaluates the radiometric accuracy of observations from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership and Sondeur Atmospherique du Profil d' Humidite Intropicale par Radiometrie (SAPHIR) onboard Megha-Tropiques through intercalibration and validation versus in situ radiosonde and Global Positioning System Radio Occultation (GPS-RO) observations. SAPHIR and ATMS water vapor channels operate at slightly different frequencies. We calculated the bias due to radiometric errors as the difference between the observed and simulated differences between the two instruments. This difference, which is often referred to as double difference, ranges between 0.3 and 0.7 K, which shows good consistency between the instruments. We used a radiative transfer model to simulate the satellite brightness temperatures (Tbs) using radiosonde and GPS-RO profiles and then compared simulated and observed Tbs. The difference between radiosonde and ATMS Tbs for the middle and upper tropospheric temperature sounding channels was less than 0.5 K at most stations, but the difference between radiosonde and ATMS/SAPHIR Tbs for water vapor channels was between 0.5 and 2.0 K. The larger bias for the water vapor channels is mainly due to several errors in radiosonde humidity observations. The mean differences between the ATMS observations and the Tbs simulated using GPS-RO profiles were 0.2, 0.3, 0.4, 0.2, and −0.2 K for channels 10–14, respectively; and the uncertainty increases from 0.02 K for channel 10 to 0.07 K for channel 14.

In previous studies published in the open literature, a strong relationship between the occurrence of hail and the microwave brightness temperatures (primarily at 37 and 85 GHz) was documented. These studies were performed with the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and most recently, the Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sensor. This led to climatologies of hail frequency from TMI and AMSR-E, however, limitations included geographical domain of the TMI sensor (35 S to 35 N) and the overpass time of the Aqua satellite (130 am/pm local time), both of which reduce an accurate mapping of hail events over the global domain and the full diurnal cycle. Nonetheless, these studies presented exciting, new applications for passive microwave sensors. NOAA and EUMETSAT have been operating the Advanced Microwave Sounding Unit (AMSU-A and -B) and the Microwave Humidity Sounder (MHS) on several operational satellites since 1998: NOAA-15 through NOAA-19; MetOp-A and -B. With multiple satellites in operation since 2000, the AMSU/MHS sensors provide near global coverage every 4 h, thus, offering a much larger time and temporal sampling than TRMM or AMSR-E. With similar observation frequencies near 30 and 85 GHz, one at 157 GHz, and additionally three at the 183 GHz water vapor band, the potential to detect strong convection associated with severe storms on a more comprehensive time and space scale exists. In this study, we develop a prototype AMSU-based hail detection algorithm through the use of collocated satellite and surface hail reports over the continental US for a 10-year period (2000–2009). Compared with the surface observations, the algorithm detects approximately 40% of hail occurrences. The simple threshold algorithm is then used to generate a hail climatology based on all available AMSU observations during 2000–2011 that is stratified in several ways, including total hail occurrence by month (March through September), total annual, and over the diurnal cycle. Independent comparisons are made compared to similar data sets derived from other satellite, ground radar and surface reports. The algorithm was also applied to global land measurements for a single year and showed close agreement with other satellite based hail climatologies. Such a product could serve as a prototype for use with a future geostationary based microwave sensor such as NASA's proposed PATH mission.

Characterization of the error associated with quantitative precipitation estimates (QPEs) from spaceborne passive microwave (PMW) sensors is important for a variety of applications ranging from flood forecasting to climate monitoring. This study evaluates the joint influence of precipitation and surface characteristics on the error structure of NASA’s Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) surface QPE product (2A12). TMI precipitation products are compared with high-resolution reference precipitation products obtained from the NOAA/NSSL ground radar–based Multi-Radar Multi-Sensor (MRMS) system. Surface characteristics were represented via a surface classification dataset derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). This study assesses the ability of 2A12 to detect, classify, and quantify precipitation at its native resolution for the 2011 warm season (March–September) over the southern continental United States. Decreased algorithm performance is apparent over dry and sparsely vegetated regions, a probable result of the surface radiation signal mimicking the scattering signature associated with frozen hydrometeors. Algorithm performance is also shown to be positively correlated with precipitation coverage over the sensor footprint. The algorithm also performs better in pure stratiform and convective precipitation events, compared to events containing a mixture of stratiform and convective precipitation within the footprint. This possibly results from the high spatial gradients of precipitation associated with these events and an underrepresentation of such cases in the retrieval database. The methodology and framework developed herein apply more generally to precipitation estimates from other passive microwave sensors on board low-Earth-orbiting satellites and specifically could be used to evaluate PMW sensors associated with the recently launched Global Precipitation Measurement (GPM) mission.

A prototype precipitation retrieval algorithm over land has been developed by utilizing 4 year National Mosaic and Multi-Sensor Quantitative Precipitation Estimation and Special Sensor Microwave Imager/Sounder coincident data sets. One of the unique features of this algorithm is using the ancillary parameters (i.e., surface type, surface temperature, land elevation, and ice layer thickness) to stratify the single database into many smaller but more homogeneous databases, in which both the surface condition and precipitation vertical structure are similar. It is found that the probability of detection (POD) increases about 8% and 12% by using stratified databases for rainfall and snowfall detection, respectively. In addition, by considering the relative humidity at lower troposphere and the vertical velocity at 700 hPa in the precipitation detection process, the POD for snowfall detection is further increased by 20.4% from 56.0% to 76.4%. The better result is evident in both ends of the retrieved rain rate when the stratified databases are used, especially when the rain rate is greater than 30 mm/h. Similarly, the retrieved snowfall rate using stratified databases also outperforms that using single database. The correlation between retrieved and observed rain rates from stratified databases is 0.63, while it is 0.42 using the single database. The root-mean-square error is reduced by 50.3% from 2.07 to 0.98 by using stratified databases. The retrieved snow rates from stratified database are also better correlated with observations and possess smaller root-mean-square error. Additionally, the precipitation overestimation from the single database over the western United States is largely mitigated when the stratified databases are utilized. It is further demonstrated that over the majority of the stratified databases, the relationship between precipitation rate and brightness temperature is much closer to that from the corresponding category in the validation databases, rather than that from the single database. Therefore, overall superior performance using the stratified databases for both the precipitation detection and retrieval is achieved.

The Goddard profiling algorithm 2010 (GPROF2010) was revised for the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) instrument. The GPROF2010 land algorithm was developed for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), which observes slightly different central frequencies than AMSR-E. A linear transfer function was developed to convert AMSR-E brightness temperatures to their corresponding TMI frequency for raining and nonraining instantaneous fields of view (IFOVs) using collocated brightness temperature and TRMM precipitation radar (PR) measurements. Previous versions of the algorithm separated rain from surface ice, snow, and desert using a series of empirical procedures. These occasionally failed to separate raining and nonraining scenes, leading to failed detection and false alarms of rain. The new GPROF2010, version 2 (GPROF2010V2), presented here, prefaced the heritage screening procedures by referencing annual desert and monthly snow climatologies to identify IFOVs where rain retrievals were unreliable. Over a decade of satellite- and ground-based observations from the Interactive Multisensor Snow and Ice Mapping System (IMS) and AMSR-E allowed for the creation of a medium-resolution (0.25° × 0.25°) climatology of monthly snow and ice cover. The scattering signature of rain over ice and snow is not well defined because of complex emissivity signals dependent on snow depth, age, and melting, such that using a static climatology was a more stable approach to defining surface types. GPROF2010V2 was subsequently used for the precipitation environmental data record (EDR) for the AMSR2 sensor aboard the Global Change Observation Mission–Water 1 (GCOM-W1).

The Joint Polar Satellite System (JPSS) Blended Products Workshop was held on the fourth day of the 2018 JPSS Science Teams Annual Conference, on August 30th, 2018, at the University of Maryland Earth System Science Interdisciplinary Center (ESSIC) in College Park, Maryland. The full agenda can be found in Appendix A. The workshop was attended by experts across agencies of satellite research and applications, such as the National Oceanic and Atmospheric Administration (NOAA)/the National Environmental Satellite, Data, and Information Service (NESDIS)/JPSS, Center for Satellite Applications and Research (STAR), Office of Satellite and Product Operations (OSPO), the National Center for Environmental Information (NCEI), the Naval Research Laboratory (NRL), Climate Prediction Center (CPC), Environmental Modeling Center (EMC), and European Centre for Medium-Range Weather Forecasts (ECMWF), and the NOAA-cooperative Institutes and industry partners.

The Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) instruments aboard the Suomi National Polar-orbiting Partnership satellite provide high-quality hyperspectral infrared and microwave observations to retrieve atmospheric vertical temperature and moisture profiles (AVTP and AVMP) and many other environmental data records (EDRs). The official CrIS and ATMS EDR algorithm, together called the Cross-track Infrared and Microwave Sounding Suite (CrIMSS), produces EDR products on an operational basis through the interface data processing segment. The CrIMSS algorithm group is to assess and ensure that operational EDRs meet beta and provisional maturity requirements and are ready for stages 1–3 validations. This paper presents a summary of algorithm optimization efforts, as well as characterization and validation of the AVTP and AVMP products using the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis, the Atmospheric Infrared Sounder (AIRS) retrievals, and conventional and dedicated radiosonde observations. The global root-mean-square (RMS) differences between the CrIMSS products and the ECMWF show that the AVTP is meeting the requirements for layers 30–300 hPa (1.53 K versus 1.5 K) and 300–700 hPa (1.28 K versus 1.5 K). Slightly higher RMS difference for the 700 hPa-surface layer (1.78 K versus 1.6 K) is attributable to land and polar profiles. The AVMP product is within the requirements for 300–600 hPa (26.8% versus 35%) and is close in meeting the requirements for 600 hPa-surface (25.3% versus 20%). After just one year of maturity, the CrIMSS EDR products are quite comparable to the AIRS heritage algorithm products and show readiness for stages 1–3 validations.

[1] The quality of humidity measurements from global operational radiosonde sensors in upper, middle, and lower troposphere for the period 2000–2011 were investigated using satellite observations from three microwave water vapor channels operating at 183.31±1, 183.31±3, and 183.31±7 GHz. The radiosonde data were partitioned based on sensor type into 19 classes. The satellite brightness temperatures (Tb) were simulated using radiosonde profiles and a radiative transfer model, then the radiosonde simulated Tb's were compared with the observed Tb's from the satellites. The surface affected Tb's were excluded from the comparison due to the lack of reliable surface emissivity data at the microwave frequencies. Daytime and nighttime data were examined separately to see the possible effect of daytime radiation bias on the sonde data. The error characteristics among different radiosondes vary significantly, which largely reflects the differences in sensor type. These differences are more evident in the mid-upper troposphere than in the lower troposphere, mainly because some of the sensors stop responding to tropospheric humidity somewhere in the upper or even in the middle troposphere. In the upper troposphere, most sensors have a dry bias but Russian sensors and a few other sensors including GZZ2, VZB2, and RS80H have a wet bias. In middle troposphere, Russian sensors still have a wet bias but all other sensors have a dry bias. All sensors, including Russian sensors, have a dry bias in lower troposphere. The systematic and random errors generally decrease from upper to lower troposphere. Sensors from China, India, Russia, and the U.S. have a large random error in upper troposphere, which indicates that these sensors are not suitable for upper tropospheric studies as they fail to respond to humidity changes in the upper and even middle troposphere. Overall, Vaisala sensors perform better than other sensors throughout the troposphere exhibiting the smallest systematic and random errors. Because of the large differences between different radiosonde humidity sensors, it is important for long-term trend studies to only use data measured using a single type of sensor at any given station. If multiple sensor types are used then it is necessary to consider the bias between sensor types and its possible dependence on humidity and temperature.

In this paper, the overall quality of the water vapor profiles of global operational radiosonde data for the period 2000-2009 is investigated using upper tropospheric humidity (UTH) retrieved from microwave satellite data. Overall, the nighttime radiosonde data showed a dry bias (- 5% to -15%) over Europe, Australia, and New Zealand and systematically moist bias (greater than 30%) over China and the former Soviet Union. The nighttime sonde data from the U.S. and Canada showed a bias between -10% and 20%. Most stations indicated a daytime radiation dry bias, except for a few stations from the U.S. and the former Soviet Union. A sensorwise comparison showed a large nighttime wet bias for the Russian (MRZ-3A and MARS) and Chinese GZZ-2 sensors, a relatively small nighttime wet bias for the U.S. Sippican and VIZ-B2 sensors, and a nighttime dry bias for the Chinese GTS1, Vaisala (RS80-A, RS80-H, RS90, RS92K, and RS92-SGP), and the U.S. VIZ-MKII sensors. All sensors had a daytime radiation dry bias, except for the Russian MRZ-3A sensor that had a daytime radiation wet bias that could be because of the daytime radiation bias correction. Because of the large differences between different radiosonde sensors, it is essential for UTH studies to only use the data measured using a single type of sensor at any given station.

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.

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.

Global monthly rainfall estimates and other hydrological products have been produced from 1987 to the present using measurements from the Defense Meteorological Satellite Program (DMSP) series of the Special Sensor Microwave Imager (SSM/I). The aim of this paper is twofold: to present the recent efforts to improve the quality control (QC) of historical antenna temperature of the SSM/I sensor (1987–2008) and how this improvement impacts the different hydrological products that are generated at NOAA/National Environmental Satellite, Data, and Information Service (NESDIS). Beginning in 2005, the DMSP Special Sensor Microwave Imager/Sounder (SSMI/S) has been successfully operating on the F-16, F-17, and F-18 satellites. The second objective of this paper is focused on the application of SSMI/S channels to evaluate the performance of several hydrological products using the heritage of existing SSM/I algorithms and to develop an improved strategy to extend the SSM/I time series into the SSMI/S era, starting with data in 2009 for F-17. The continuity of hydrological products from SSM/I to SSMI/S has shown to be a valuable contribution for the precipitation and climate monitoring community but several sensor issues must be accounted for to meet this objective.

Passive microwave (PMW) satellite-based precipitation over land algorithms rely on physical models to define the most appropriate channel combinations to use in the retrieval, yet typically require considerable empirical adaptation of the model for use with the satellite measurements. Although low-frequency channels are better suited to measure the emission due to liquid associated with rain, most techniques to date rely on high-frequency, scattering-based schemes since the low-frequency methods are limited to the highly variable land surface background, whose radiometric contribution is substantial and can vary more than the contribution of the rain signal. Thus, emission techniques are generally useless over the majority of the Earth's surface. As a first step toward advancing to globally useful physical retrieval schemes, an intercomparison project was organized to determine the accuracy and variability of several emissivity retrieval schemes. A three-year period (July 2004-June 2007) over different targets with varying surface characteristics was developed. The PMW radiometer data used includes the Special Sensor Microwave Imagers, SSMI Sounder, Advanced Microwave Scanning Radiometer (AMSR-E), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Advanced Microwave Sounding Units, and Microwave Humidity Sounder, along with land surface model emissivity estimates. Results from three specific targets in North America were examined. While there are notable discrepancies among the estimates, similar seasonal trends and associated variability were noted. Because of differences in the treatment surface temperature in the various techniques, it was found that comparing the product of temperature and emissivity yielded more insight than when comparing the emissivity alone. This product is the major contribution to the overall signal measured by PMW sensors and, if it can be properly retrieved, will improve the utility of emission techniques for over land precipitation retrievals. As a more rigorous means of comparison, these emissivity time series were analyzed jointly with precipitation data sets, to examine the emissivity response immediately following rain events. The results demonstrate that while the emissivity structure can be fairly well characterized for certain surface types, there are other more complex surfaces where the underlying variability is more than can be captured with the PMW channels. The implications for Global Precipitation Measurement-era algorithms suggest that physical retrievals are feasible over vegetated land during the warm seasons.

A 1-D variational system has been developed to process spaceborne measurements. It is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. One of the particularities of the system is its applicability in cloudy and precipitating conditions. Although valid, in principle, for all sensors for which the radiative transfer model applies, it has only been tested for passive microwave sensors to date. The Microwave Integrated Retrieval System (MiRS) inverts the radiative transfer equation by finding radiometrically appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature. The inclusion of the emissivity spectrum in the state vector makes the system applicable globally, with the only differences between land, ocean, sea ice, and snow backgrounds residing in the covariance matrix chosen to spectrally constrain the emissivity. Similarly, the inclusion of the cloud and hydrometeor parameters within the inverted state vector makes the assimilation/inversion of cloudy and rainy radiances possible, and therefore, it provides an all-weather capability to the system. Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background. In the MiRS, the fundamental products are inverted first and then are interpreted into secondary or derived products such as sea ice concentration, snow water equivalent (based on the retrieved emissivity) rainfall rate, total precipitable water, integrated cloud liquid amount, and ice water path (based on the retrieved atmospheric and hydrometeor products). The MiRS system was implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 for the NOAA-18 satellite. Since then, it has been extended to run for NOAA-19, Metop-A, and DMSP-F16 and F18 SSMI/S. This paper gives an overview of the system and presents brief results of the assessment effort for all fundamental and derived products.

A prototype generic, unified land surface classification and screening methodology for Global Precipitation Measurement (GPM)-era microwave land precipitation retrieval algorithms by using ancillary datasets is developed. As an alternative to the current radiometer-determined approach, the new methodology is shown to be promising in improving rain detection by providing better surface-cover-type information. The early prototype new surface screening scheme was applied to the current version of the Goddard profiling algorithm that is used for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (GPROFV6). It has shown improvements in surface-cover-type classification and hence better precipitation retrieval comparisons with TRMM precipitation radar level-2 (L2) (2A25) data and the Global Precipitation Climatology Project (GPCP) version-2.1 (GPCPV2.1) datasets. The new ancillary data approach removes the current dependency of the screening step on relatively different satellite-specific channels and ensures the comparability and continuity of satellite-based precipitation products from different platforms. This is particularly important for advancing the current state of precipitation retrieval over land and for use in merged rainfall products.

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 paper describes improvements to the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) land rainfall algorithm in version 7 (v7) of the TRMM data products. The correlations between rain rates and TMI 85-GHz brightness temperatures (Tb) for convective and stratiform rain are generated using 7 years of collocated TMI and TRMM precipitation radar (PR) data. The TMI algorithm for estimating the convective ratio of rainfall is also modified. This paper highlights both the improvements in the v7 algorithm and the continuing problems with the land rainfall retrievals. It is demonstrated that the proposed changes to the algorithm significantly lower the overestimation by TMI globally and over large sections of central Africa and South America. Also highlighted are the problems with the 2A12 land algorithm that have not been addressed in the version 7 algorithm, such as large regional and seasonal dependence of biases in the TMI rain estimates, and potential changes to the algorithm to resolve these problems are discussed.

Global monthly rainfall estimates have been produced from more than 20 years of measurements from the Defense Meteorological Satellite Program series of Special Sensor Microwave Imager (SSM/I). This is the longest passive microwave dataset available to analyze the seasonal, annual, and interannual rainfall variability on a global scale. The primary algorithm used in this study is an 85-GHz scattering-based algorithm over land, while a combined 85-GHz scattering and 19/37-GHz emission is used over ocean. The land portion of this algorithm is one of the components of the blended Global Precipitation Climatology Project rainfall climatology. Because previous SSM/I processing was performed in real time, only a basic quality control (QC) procedure had been employed to avoid unrealistic values in the input data. A more sophisticated, statistical-based QC procedure on the daily data grids (antenna temperature) was developed to remove unrealistic values not detected in the original database and was employed to reprocess the rainfall product using the current version of the algorithm for the period 1992–2007. Discrepancies associated with the SSM/I-derived monthly rainfall products are characterized through comparisons with various gauge-based and other satellite-derived rainfall estimates. A substantial reduction in biases was observed as a result of this QC scheme. This will yield vastly improved global rainfall datasets.

The Chesapeake Bay (CB) is the largest estuary in North America and has been listed as impaired under the Clean Water Act since 1998. Deteriorating water conditions are largely due to contaminants carried into the Bay by the many tributaries in the CB watershed. The Earth System Science Interdisciplinary Center of the University of Maryland at College Park is developing a Chesapeake Bay Forecast System (CBFS) to provide regional Earth System predictions for the Chesapeake Bay watershed. SWAT is utilized as the land module of CBFS to simulate the hydrology and water quality of the prominent tributaries in the CB watershed. This aticle reports the model configuration as well as calibration and validation results for Rappahannock, one of the major CB river basins. The complete configuration of the model involved the following steps: watershed delineation and the establishment of hydrologic response units, sensitivity analysis, balancing water budget, adjusting crop yields, balancing flow partition, manual and auto-calibration, and validation. The simulated quantities include daily average streamflow and daily loadings of sediment, nitrate, and phosphate. The calibration NSE for the four variables were, respectively, 0.73, 0.63, 0.52, and 0.50 which met a set of stringent evaluation criteria set forth by Moriasi et al. (2007) as being good or satisfactory simulations. NSE measures how well the plot of observed versus simulated data fits the 1:1 line and a value of 1 indicates a perfect simulation. The calibration results also satisfied the other evaluation criteria used in this study with the exception of large uncertainty in phosphate load. Validation of streamflow and nitrate load also met all the evaluation criteria including NSE being 0.70 and 0.61, respectively. The validation results for sediment and phosphate loads were considered unsatisfactory with NSE being 0.25 and 0.19. The model statistics were compared to those of the Chesapeake Bay Program Watershed Model and showed strength in the simulations of Rappahannock River streamflow, sediment load, and nitrate load. The Rappahannock SWAT model currently produces routine 14-day ensemble forecasts of daily flow and loadings of sediment, nitrate and phosphate in an automated system. Four thresholds were used to compute the ETS skill score of the Rappahannock streamflow forecast: 0.5, 1.0, 1.5, and 2.0 times of the average daily flow. The ETS skill scores of the first day forecast were 0.75, 0.56, 0.52, and 0.41, respectively, for the four thresholds. They remain around or above 0.4 up to 14, 8, 7, and 5 days lead time in the four cases. The ETS scores reveal that the model has good forecast skill for base flow and noticeable skill for event flow. The forecast skill remains visible for at least 2 weeks for base flow and at least 5 days for the event flow thresholds used in this study.

The importance of analyzing climate at high spatiotemporal resolution has been emphasized in the recent Intergovernmental Panel on Climate Change (IPCC) report. Several high-resolution analyses of global precipitation have recently been created to meet this need by combining high-quality passive microwave estimates with frequently sampled geosynchronous infrared estimates. A new daily 0.25° analysis has been developed at the Cooperative Institute for Climate Studies (CICS), called the CICS High-Resolution Optimally Interpolated Microwave Precipitation from Satellites (CHOMPS), which is based only on passive microwave satellite estimates. The analysis was developed using all available sensors and the most up-to-date common retrieval scheme. An important advantage of CHOMPS is, therefore, its consistency. The microwave estimates from the different sensors at hourly time scales are combined using optimum interpolation (OI), using estimates of the noise and spatial correlation scales and standardized analysis weights. This technique reduces the random errors while still capturing the tails of the distribution of precipitation and provides estimates of output error based on the noise-to-signal ratio of the inputs. Hourly values are combined to produce a daily average and error estimate. Evaluation of CHOMPS against surface-based observational data, such as the stage IV radar and the Tropical Atmosphere Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TRITON) gauges, indicates that CHOMPS performs well, especially when compared to other high-resolution products. This analysis, therefore, highlights OI as a feasible methodology for the creation of a high-resolution precipitation product. The analysis begins in 1998 and has thus far been continued through 2007, making it a useful dataset to examine intraseasonal variability. When the period of the data becomes long enough, it will prove to be a useful dataset to study longer modes of climate variability.

Satellites offer an unrivaled vantage point to observe and measure Earth system processes and parameters. Observations of meteorological phenomena permit a more holistic view of the weather and climate that is not possible through conventional surface observations. Precipitation (rain and snow) in particular, benefit from such observations since precipitation is spatially and temporally highly variable: conventional gauge and radar measurements tend to be land-based with variable coverage. This paper provides an overview of the satellite systems that provide the observations, the techniques used to derive precipitation from the observations, and examples of the precipitation products available for users to access.

The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) 2A12 product consists of unique components configured for land and oceanic precipitation retrievals. This design was based on the vastly different physical characteristics of the retrieval, involving primarily emission over ocean and entirely scattering over land. This paper describes the current status of the TRMM Version 6 (V6) 2A12 product over land and envisioned improvements for TRMM TMI V7 and GPM GMI V1. On a global scale, the 2A12 land algorithm exhibits biases when compared with the TRMM 2A25 (Precipitation Radar (PR) based) and rain gauges. These range from 6 percent for GPCC to 20 percent for 2A25. Closer comparison also reveals regional and seasonal biases, with the largest positive biases found in warm-season convective zones and over semi-arid regions. Some negative biases are found in warm-rain precipitation regimes where scattering at 85 GHz is unable to detect a precipitation signal. On an instantaneous time scale, 2A12 land also produces a positive bias when compared with high-quality radar data from Melbourne, Florida, a TRMM ground validation site. The largest discrepancies occur for rain rates of less than 2 mm h -1 . A number of known “anomalies” are highlighted, including overestimation of rainfall in deep convective systems, underestimation in warm-rain regimes, and a number of features associated with the

Over the last 15 years the constellation of satellites carrying passive microwave (PMW) sensors has grown to a mature collection of almost a dozen satellites at any given time. Increasingly, a broad range of science and user communities have come to depend on the quasi-global precipitation analyses that intercalibrate and merge these individual PMW precipitation data streams. At present, the constellation of precipitation-relevant conical and cross-track scanning multichannel PMW instruments depends on many satellites past their design life and in continued operation by the responsible agencies. The Group on Earth Observations (GEO) Water Strategy and subsequent discussions in Coordinating Group for Meteorological Satellites (CGMS) and Committee on Earth Observing Systems (CEOS) have raised the issue of how a robust future precipitation constellation should be constructed. We discuss how retrievals are impacted by sensor resolution and channel diversity, the observation interval, and the use of a quasi-operational satellite precipitation radar for calibration. Specifically: 1) Sensor footprints larger than about 5-10 km start to introduce significant amounts of non-linearity in the retrievals, the so-called beam-filling problem. 2) Channel diversity has been shown to be necessary for covering the range of precipitation rates and types (liquid vs. solid). As well, diverse polarization at a given frequency is also important. 3) An observation interval less than three hours for every time around the day barely accommodates the required revist times for cloud-scale precipitating systems. 4) The precipitation radars on the precessing TRMM and GPM Core Observatory satellites have demonstrated the utility of routine calibration for precipitation estimates across all the PMW sensors (and in the case of GPM this is done for most of the Earth's climate zones). Such considerations are critical to the discussion on how to shift to a new, more diverse generation of precipitation-relevant sensors while preserving the characteristics that provide (and support continued innovation of) quality PMW retrievals and value-added products that many users find attractive.

Cloud droplet effective radius (DER) and liquid water path (LWP) are two key parameters for the quantitative assessment of cloud effects on the exchange of energy and water. Chang and Li presented an algorithm using multichannel measurements made at 3.7, 2.1, and 1.6 μm to retrieve a cloud DER vertical profile for improved cloud LWP estimation. This study applies the multichannel algorithm to the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data on the Aqua satellite, which also carries the Advanced Microwave Scanning Radiometer (AMSR-E) for measuring cloud LWP and precipitation. By analyzing one day of coincident MODIS and AMSR-E observations over the tropical oceans between 40°S and 40°N for overcast warm clouds (>273 K) having optical depths between 3.6 and 23, the effects of DER vertical variation on the MODIS-derived LWP are reported. It is shown that the LWP tends to be overestimated if the DER increases with height within the cloud and underestimated if the DER decreases with height within the cloud. Despite the uncertainties in both MODIS and AMSR-E retrievals, the result shows that accounting for the DER vertical variation reduces the mean biases and root-mean-square errors between the MODIS- and AMSR-E–derived LWPs. Besides, the manner in which the DER changes with height has the potential for differentiating precipitative and nonprecipitative warm clouds. For precipitating clouds, the DER at the cloud top is substantially smaller than the DER at the cloud base. For nonprecipitating clouds, however, the DER differences between the cloud top and the cloud base are much less.

The retrieval of Snow Water Equivalent (SWE) from remote sensing satellites continues to be a very challenging problem. In this paper, we evaluate the accuracy of a new SWE product derived from the blending of a passive microwave SWE product based on the Advanced Microwave Sounding Unit (AMSU) with a multi-sensor snow cover extent product based on the Interactive Multi-sensor Snow and Ice Mapping System (IMS). The microwave measurements have the ability to penetrate the snow pack, and thus, the retrieval of SWE is best accomplished using the AMSU. On the other hand, the IMS maps snow cover more reliably due to the use of multiple satellite and ground observations. The evolution of global snow cover from the blended, the AMSU and the IMS products was examined during the 2006 snow season. Despite the overall good inter-product agreement, it was shown that the retrievals of snow cover extent in the blended product are improved when using IMS, with implications for improved microwave retrievals of SWE. In a separate investigation, the skill of the microwave SWE product was also examined for its ability to correctly estimate SWE globally and regionally. Qualitative evaluation of global SWE retrievals suggested dependence on land surface temperature: the lower the temperature, the higher the SWE retrieved. This temperature bias was attributed in part to temperature effects on those snow properties that impact microwave response. Therefore, algorithm modifications are needed with more dynamical adjustments to account for changing snow cover. Quantitative evaluation over Slovakia in central Europe, for a limited period in 2006, showed reasonably good performance for SWE less than 100 mm. Sensitivity to deeper snow decreased significantly. Copyright © 2007 John Wiley & Sons, Ltd.

A recurring challenge for microwave precipitation retrieval algorithms is the separation of precipitation from surface snow. Cold surfaces can have similar radiometric characteristics to suspended precipitation particles, such that surface snow reduces the detection accuracy of precipitation algorithms. Snow detection is typically performed using a collection of heritage algorithms, however surface snow can often go undetected and lead to erroneous precipitation retrievals. This study examines flagging procedures in the Goddard Profiling Algorithm 2010 V2 as it applies to the Advanced Scanning Microwave Radiometer 2 over the United States in the winter of 2014–2015. The legacy algorithms are often not reliable, such that a climatological screen is applied in known snowy regions. The diurnal cycle of brightness temperatures also complicates resolving rain when using the static legacy algorithms.

Rain-rate retrievals from passive microwave sensors are useful for a number of applications related to weather forecasting. For example, in the United States, such estimates are useful for offshore rainfall systems approaching land and in regions where the Weather Surveillance Radar-1988 Doppler (WSR-88D) network is inadequate. Improvements have been made to the rain-rate retrieval from the Advanced Microwave Sounding Unit (AMSU) on board the National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites (POESs). The new features of the improved rain-rate algorithm include a two-stream correction of the satellite brightness temperatures at 89 and 150 GHz, cloud- and rain-type classification for better retrieval of rain rate, and removal of the two ad hoc thresholds in the ice water path (IWP) and effective diameter (De) retrieval where the scattering signals are very small. In this paper, the new algorithm has been compared to the previous NOAA operational algorithm. In particular, the better utilization of the measurements at and above 150 GHz is shown to produce improved sensitivity to light rainfall associated with winter season storm systems. This improvement is demonstrated through a wintertime case study over southern California during February 2003.

Inland flooding caused by heavy rainfall from landfalling tropical cyclones is a significant threat to life and property. The tropical rainfall potential (TRaP) technique, which couples satellite estimates of rain rate in tropical cyclones with track forecasts to produce a forecast of 24-h rainfall from a storm, was developed to better estimate the magnitude of this threat. This paper outlines the history of the TRaP technique, details its current algorithms, and offers examples of its use in forecasting. Part II of this paper covers verification of the technique.

Satellite analysts at the Satellite Services Division (SSD) of the National Environmental, Satellite, Data, and Information Service (NESDIS) routinely generate 24-h rainfall potential for all tropical systems that are expected to make landfall within 24 to at most 36 h and are of tropical storm or greater strength (>65 km h−1). These estimates, known as the tropical rainfall potential (TRaP), are generated in an objective manner by taking instantaneous rainfall estimates from passive microwave sensors, advecting this rainfall pattern along the predicted storm track, and accumulating rainfall over the next 24 h. In this study, the TRaPs generated by SSD during the 2002 Atlantic hurricane season have been validated using National Centers for Environmental Prediction (NCEP) stage IV hourly rainfall estimates. An objective validation package was used to generate common statistics such as correlation, bias, root-mean-square error, etc. It was found that by changing the minimum rain-rate threshold, the results could be drastically different. It was determined that a minimum threshold of 25.4 mm day−1 was appropriate for use with TRaP. By stratifying the data by different criteria, it was discovered that the TRaPs generated using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rates, with its optimal set of measurement frequencies, improved spatial resolution, and advanced retrieval algorithm, produced the best results. In addition, the best results were found for TRaPs generated for storms that were between 12 and 18 h from landfall. Since the TRaP is highly dependent on the forecast track of the storm, selected TRaPs were rerun using the observed track contained in the NOAA/Tropical Prediction Center (TPC) “best track.” Although some TRaPs were not significantly improved by using this best track, significant improvements were realized in some instances. Finally, as a benchmark for the usefulness of TRaP, comparisons were made to Eta Model 24-h precipitation forecasts as well as three climatological maximum rainfall methods. It was apparent that the satellite-based TRaP outperforms the Eta Model in virtually every statistical category, while the climatological methods produced maximum rainfall totals closer to the stage IV maximum amounts when compared with TRaP, although these methods are for storm totals while TRaP is for a 24-h period.

This study compares monthly total precipitable water (TPW) from the National Aeronautics and Space Administration (NASA) Water Vapor Project (NVAP) and reanalyses of the National Centers for Environmental Prediction (NCEP) (R-1), NCEP–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) (R-2), and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) from January 1988 through December 1999. Based on the means, NVAP exhibits systematic wetter land regions relative to the other datasets reflecting differences in their analyses due to paucity in radiosonde observations. ERA-40 is wetter in the atmospheric convergence zones than the U.S. reanalyses and NVAP ranges in between. Differences in the annual cycle between the reanalyses (especially R-2) and NVAP are also noticeable over the tropical oceans. Analyses on the interannual variabilities show that the ENSO-related spatial pattern in ERA-40 follows more coherently that of NVAP than those of the U.S. reanalyses. The 1997/98 El Niño’s effect on TPW is shown to be strongest only in NVAP, R-1, and ERA-40 during the period of study. All the datasets show TPW decreases in the Tropics following the 1991 Mt. Pinatubo eruption. By subtracting SST-estimated TPW from the datasets, only NVAP and ERA-40 can well represent the spatial pattern of convergence and/or moist-air advection zones in the Tropics. Even though all the datasets are viable for water cycle and climate analyses with discrepancies (wetness and dryness) to be aware of, this study has found that NVAP and ERA-40 perform better than the U.S. reanalyses during the 12-yr period.

The microwave coastal rain identification procedure that has been used by NASA for over 10 yr, and also more recently by NOAA, for different instruments beginning with the Special Sensor Microwave Imager (SSM/I), is updated for use with Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer (AMSR)-[Earth Observing System (EOS)] E microwave data. Since the development of the SSM/I algorithm, a wealth of both space-based and ground-based radar-rainfall estimates have become available, and here some of these data are used with collocated TMI and AMSR-E data to improve the estimation of coastal rain areas from microwave data. Two major improvements are made. The first involves finding the conditions where positive rain rates should be estimated rather than leaving the areas without estimates as in the previous algorithm. The second is a modification to the final step of the rain identification method; previously, a straight brightness temperature cutoff was used, but this is modified to a polarization-corrected temperature criterion. These modifications are made for the TRMM version 6 product release and the third (1 September) release of AMSR-E products to the public, both in 2004. The modifications are slightly different for each of these two sensors.

As part of the Global Precipitation Climatology Project (GPCP), analyses of pentad precipitation have been constructed on a 2.5° latitude–longitude grid over the globe for a 23-yr period from 1979 to 2001 by adjusting the pentad Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) against the monthly GPCP-merged analyses. This adjustment is essential because the precipitation magnitude in the pentad CMAP is not consistent with that in the monthly CMAP or monthly GPCP datasets primarily due to the differences in the input data sources and merging algorithms, causing problems in applications where joint use of the pentad and monthly datasets is necessary. First, pentad CMAP-merged analyses are created by merging several kinds of individual data sources including gauge-based analyses of pentad precipitation, and estimates inferred from satellite observations. The pentad CMAP dataset is then adjusted by the monthly GPCP-merged analyses so that the adjusted pentad analyses match the monthly GPCP in magnitude while the high-frequency components in the pentad CMAP are retained. The adjusted analyses, called the GPCP-merged analyses of pentad precipitation, are compared to several gauge-based datasets. The results show that the pentad GPCP analyses reproduced spatial distribution patterns of total precipitation and temporal variations of submonthly scales with relatively high quality especially over land. Simple applications of the 23-yr dataset demonstrate that it is useful in monitoring and diagnosing intraseasonal variability. The Pentad GPCP has been accepted by the GPCP as one of its official products and is being updated on a quasi-real-time basis.

Three related algorithms have been developed to retrieve rainfall from the Advanced Microwave Scanning Radiometer-Earth Observing System observations. First, land and ocean backgrounds must be treated separately. The highly reflective ocean background is ideal for observing atmospheric constituents including rainfall. The high and variable emissivity of the land background limits the usefulness of the signal from the liquid hydrometeors under most conditions. For an algorithm with global applicability, it is more reasonable to use the signal generated by scattering of microwaves from frozen hydrometeors. Unfortunately, this scattering is related to rainfall only indirectly. This dichotomy is reinforced by the differing availability of ground truth in the two environments. Thus, two distinct approaches must be used in generating the algorithms for the two cases. The oceanic algorithm is based on physical models, and the land algorithm is generated empirically. Functionally, the two algorithms are merged into a common structure for retrievals on a pixel-by-pixel (Level-2) basis, but the distinct philosophies remain. The third element is the monthly total product (Level-3) which could be generated by simply summing the Level-2 products. However, subtle biases, unimportant on a Level-2 basis, could introduce serious errors into the totals. Therefore, we have chosen to generate a separate product over the oceans consisting of monthly averaged rain rates over 5/spl deg/ /spl times/5/spl deg/ boxes. The Level-3 ocean algorithm assumes that the rainfall intensity is distributed log-normally and also absorbs small instrumental calibration errors and some modeling errors.

A bias-adjusted radar rainfall product is created and used for evaluation of two satellite rainfall estimation algorithms. Three years of collocated rainfall estimates from radar, rain gauges, a microwave satellite algorithm, and a multispectral (visible through near-infrared) algorithm were collected over the continental United States from July 1998 through July 2001. The radar and gauge data are compared to determine the locations and times at which the rainfall occurrences estimated by these two sensors are in sufficient agreement for the data to be used for validation. This procedure serves as quality control for both sensors and determines the locations at which the radar has difficulty detecting rainfall and should not be used in a validation dataset. For the data remaining after quality control, the gauge data are used for multiplicative adjustment of the radar estimates to remove the radar bias with respect to the gauges. These bias-adjusted estimates are compared with the satellite rainfall estimates to observe the evolution of the satellite biases over the 3-yr period. The multispectral algorithm was under development throughout the 3-yr period, and improvement is evident. The microwave algorithm overestimates rainfall in the summer months, underestimates in the winter months, and has an east-to-west bias gradient, all of which are consistent with physical explanations and previous findings. The multispectral algorithm bias depends highly on diurnal sampling; there is much greater overestimation for the daytime overpasses. These results are applicable primarily to the eastern half of the United States, because few data in the western half remain after quality control.

Microwave polarization and gradient ratio (MPGR) is an effective indicator for characterizing the land surface from sensors like EOS Advanced Microwave Scanning Radiometer (AMSR-E). Satellite-generated brightness temperatures (BT) are largely influenced by soil moisture and vegetation cover. The MPGR combines the microwave gradient ratio with polarization ratio to determine surface characteristics (i.e., bare soil/developed, ice, and water) and under cloud covered conditions when this information cannot be obtained using optical remote sensing data. This investigation uses the HDF Explorer, Matlab, and ArcGIS software to process the pixel latitude, longitude, and BT information from the AMSR-E imagery. This paper uses the polarization and gradient ratio from AMSR-E BT for 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0 GHz to identify seventeen land cover types. A smaller MPGR indicates dense vegetation, with the MPGR increasing progressively for mixed vegetation, degraded vegetation, bare soil/developed, and ice and water. This information can help improve the characterization of land surface phenology for use in weather forecasting applications, even during cloudy and precipitation conditions which often interferes with other sensors.

This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithm's poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective–stratiform classification scheme, the new algorithm also makes use of emission and scattering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the sc...

The frequencies flown on the Special Sensor Microwave Imager (SSM/I) are sensitive to liquid water near the earth’s surface. These frequencies are primarily atmospheric window channels, which receive the majority of their radiation from the surface. Liquid water near the surface depresses the emissivity as a function of wavelength. The relationship between brightness temperatures at different frequencies is used to dynamically derive the amount of liquid water in each SSM/I observation at 1/38 resolution. These data are averaged at 18 resolution throughout the globe for each month during the period of 1992‐97, and the 6-yr monthly means and the monthly anomalies of the wetness index are computed from this base period. To quantify the relationship between precipitation and surface wetness, these anomalies are compared with precipitation anomalies derived from the Global Precipitation Climate Program. The analysis was performed for six agricultural regions across six continents. There is generally a good correspondence between the two variables. The correlation generally increases when the wetness index is compared with precipitation anomalies accumulated over a 2-month period. These results indicate that the wetness index has a strong correspondence to the upper layer of the soil moisture in many cultivated areas of the world. The region in southeastern Australia had the best relationship, with a correlation coefficient of 0.76. The Sahel, France, and Argentina showed that the wetness index had memory of precipitation anomalies from the previous months. The memory is shorter for southeastern Australia and central China. The weakest correlations occurred over the southeastern United States, where the surface is covered by dense vegetation. The unique signal, strengths, and weaknesses of the wetness index in each of the six study regions are discussed.

The advanced microwave sounding unit (AMSU) was finally launched in May 1998 aboard the NOAA 15 satellite. Algorithms are provided for retrieving the total precipitable water (TPW) and cloud liquid water (CLW) over oceans using the AMSU measurements at 23.8 and 31.4 GHz. Extensive comparisons are made between the AMSU retrievals of CLW and TPW and those obtained using other satellite instruments (Special Sensor Microwave Imager (SSM/I) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)) and ground-based radiometers. The AMSU TPW is also compared against radiosonde data, where all of the results are in good agreement with rms differences less than 3 mm and biases less than 1 mm over the range between 5 and 60 mm. The CLW comparisons show greater variability, although the time series of the AMSU and ground-based sensors follow each other and cover the same dynamic range of 0 - 0.5 mm. The AMSU CLW also compares well with the other satellite measurements, although a bias exists between AMSU and TMI when the CLW exceeds 0.5 mm.

Window channel measurements from the NOAA-15 Advanced Microwave Sounding Unit (AMSU) are used to retrieve rain rates. This study focuses on the rain rate retrievals over land and uses measurements at 89 and 150 GHz to detect a scattering signal by millimeter-sized ice particles under precipitating atmospheres. An operational algorithm using the AMSU-A module (89 GHz) implemented in August 1999 is also described. The algorithms are calibrated with hourly surface rain observations. It is shown that identification of stratiform rain can be dramatically improved using measurements at 150 GHz from the AMSU-B sensor. Improved spatial resolution of AMSU-B is a major factor contributing to this algorithm.

Until recently, monthly rainfall products using the National Oceanic and Atmospheric Administration National Environmental Satellite, Data, and Information Service Office of Research and Applications Special Sensor Microwave Imager (SSM/I) rainfall algorithm have been generated on a global 2.5° × 2.5° grid. The rainfall estimates are based on a subsampled set of the full-resolution SSM/I data, with a resulting spatial density of about one-third of what is possible at SSM/I’s highest spatial resolution. The reduction in the spatial resolution was introduced in 1992 as a compromise dictated by data processing capabilities. Currently, daily SSM/I data processing at full resolution has been established and is being operated in parallel with the subsampled set. Reprocessing of the entire SSM/I time series based on the full-resolution data is plausible but requires the reprocessing of over 10 yr of retrospective data. Because the Global Precipitation Climatology Project is considering the generation of a daily 1° × 1° rainfall product, it is important that the effects of using the reduced spatial resolution be reexamined. In this study, error due to using the reduced-resolution versus the full-resolution SSM/I data in the gridded products at 2.5° and 1° grid sizes is examined. The estimates are based on statistics from radar-derived rain data and from SSM/I data taken over the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) radar site. SSM/I data at full resolution were assumed to provide rain estimates with 12.5-km spacing. Subsampling with spacings of 25, 37.5 (which corresponds to the present situation of ⅓° latitude–longitude spatial resolution), and 50 km were considered. For the instantaneous 2.5° × 2.5° product, the error due to subsampling, expressed as a percentage of the gridbox mean, was estimated using radar-derived data and was 6%, 10%, and 15% at these successively poorer sampling densities. For monthly averaged products on a 2.5° × 2.5° grid, it was substantially lower: 3%, 4%, and 7%, respectively. Subsampling errors for monthly averages on a 1° × 1° grid were 8%, 16%, and 23%, respectively. Estimates based on SSM/I data at full resolution gave errors that were somewhat larger than those from radar-based estimates. It was concluded that a rain product of monthly averages on a 1° × 1° grid must use the full-resolution SSM/I data. More work is needed to determine how applicable these estimates are to other areas of the globe with substantially different rain statistics.

The success of any passive microwave precipitation retrieval algorithm relies on the proper identification of rain areas and the elimination of surface areas that produce a signature similar to that of precipitation. A discussion on the impact of and on methods that identify areas of rain, snow cover, deserts, and semiarid conditions over land, and rain, sea ice, strong surface winds, and clear, calm conditions over ocean, are presented. Additional artifacts caused by coastlines and Special Sensor Microwave/Imager data errors are also discussed, and methods to alleviate their impact are presented. The strengths and weaknesses of the “screening” techniques are examined through application on various case studies used in the WetNet PIP-2. Finally, a methodology to develop a set of screens for use as a common rainfall indicator for the intercomparison of the wide variety of algorithms submitted to PIP-2 is described.