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This report is a result of an interesting journey that originated in discussions between Rémy Roca, chair of Global Energy and Water Exchanges (GEWEX) Data and Analysis Panel (GDAP), and Ziad Haddad, chair of International Precipitation Working Group (IPWG), during visits to Toulouse and to Pasadena as well as during many of the various workshops and conferences dedicated to precipitation. It culminated in the present report built from contributions by more than 20 scientists from all over the world. The first written draft was completed immediately following an open meeting for interested contributors that took place on a Saturday morning in a public library in the heights of San Diego (Haddad and Roca, 21 October 2017). From this initial plan, many objectives were successfully achieved without modification, while a few required maturation before we were able to include them in the completed assessment, all of this in spite of the ongoing pandemic. The current report completes the GEWEX assessment portfolio and follows the previous IPWG assessment by 10+ years (Gruber and Levizzani, 2008). Hopefully it is the first of a more frequent and regular series of precipitation assessments.

A model evaluation approach is proposed in which weather and climate prediction models are analyzed along a Pacific Ocean cross section, from the stratocumulus regions off the coast of California, across the shallow convection dominated trade winds, to the deep convection regions of the ITCZ—the Global Energy and Water Cycle Experiment Cloud System Study/Working Group on Numerical Experimentation (GCSS/WGNE) Pacific Cross-Section Intercomparison (GPCI). The main goal of GPCI is to evaluate and help understand and improve the representation of tropical and subtropical cloud processes in weather and climate prediction models. In this paper, a detailed analysis of cloud regime transitions along the cross section from the subtropics to the tropics for the season June–July–August of 1998 is presented. This GPCI study confirms many of the typical weather and climate prediction model problems in the representation of clouds: underestimation of clouds in the stratocumulus regime by most models with the corresponding consequences in terms of shortwave radiation biases; overestimation of clouds by the 40-yr ECMWF Re-Analysis (ERA-40) in the deep tropics (in particular) with the corresponding impact in the outgoing longwave radiation; large spread between the different models in terms of cloud cover, liquid water path and shortwave radiation; significant differences between the models in terms of vertical cross sections of cloud properties (in particular), vertical velocity, and relative humidity. An alternative analysis of cloud cover mean statistics is proposed where sharp gradients in cloud cover along the GPCI transect are taken into account. This analysis shows that the negative cloud bias of some models and ERA-40 in the stratocumulus regions [as compared to the first International Satellite Cloud Climatology Project (ISCCP)] is associated not only with lower values of cloud cover in these regimes, but also with a stratocumulus-to-cumulus transition that occurs too early along the trade wind Lagrangian trajectory. Histograms of cloud cover along the cross section differ significantly between models. Some models exhibit a quasi-bimodal structure with cloud cover being either very large (close to 100%) or very small, while other models show a more continuous transition. The ISCCP observations suggest that reality is in-between these two extreme examples. These different patterns reflect the diverse nature of the cloud, boundary layer, and convection parameterizations in the participating weather and climate prediction models.

Passive microwave (PMW) radiometric observations of tropical cyclones (TCs) from the special sensor microwave imager/sounder (SSMIS) continue the legacy monitoring capabilities initiated with the special sensor microwave/imager (SSM/I) that began in 1987. The SSMIS has the following several important differences that should be factored into applications when compared to SSM/I data: 1) channel changes from 85 to 91 GHz result in a 2-8-K brightness temperature depression for many TC inner core scenes; 2) the inclusion of bore-sighted 150-GHz data can help in detecting rapidly growing convective cells that are frequently obscured by upper level clouds in visible and infrared data and are often associated with rapid intensification; and 3) the sensor swath increases by 300 km and permits enhanced spatial and temporal coverage of global TCs. All three attributes can be incorporated to maintain and/or enhance the satellite analyst's ability to monitor critical TC structure via these PMW observations.

Passive microwave (PMW) radiometric observations of clouds from multichannel imaging sensors onboard low Earth-orbiting environmental satellites are now a vital operational dataset. The first operational passive microwave sensor was the Special Sensor Microwave/Imager onboard the Defense Meteorological Satellite Program satellites, which has been gathering hydrological data records since 1987, and continued with the Tropical Rainfall Measuring Mission (TRMM) and the Advanced Microwave Scanning Radiometer onboard Aqua. These sensors view the underlying scene with an Earth incidence angle near 53/spl deg/ and with a variable azimuthal angle, depending upon the orbit direction and scan position. The WindSat sensor onboard the Coriolis satellite, launched in January 2003, is a five-channel polarimetric PMW radiometer designed to optimize ocean surface wind vector retrievals. While it does not have 85-GHz channels, an added feature is its unique fore-aft viewing capability across a portion of its fore scan swath. This provides a view of the underlying scene from two separate azimuthal directions, which provides added information on the three-dimensional (3-D) structure of clouds and their evolution. In this paper, we compare WindSat and TRMM Precipiation Radar observations of tropical cyclones (TCs) with Monte Carlo radiative transfer simulations performed on idealized 3-D convective cloud structures. The TC 3-D structure and possible tilt in the convective cloud structure are inferred from the difference between the 37-GHz equivalent blackbody brightness temperatures (T/sub B/) from the corresponding fore and aft view observations. The information gained from this analysis is important since asymmetries in the cloud vertical and horizontal structure may be an indication of upper level wind shear, which plays a major role in influencing changes of the TC intensity.

The multispectral sensing capabilities afforded by the 36-channel Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Earth Observing System (EOS) Terra and Aqua satellites have the potential to improve satellite-derived cloud and precipitation products. Included in this channel suite are spectral bands having particular sensitivity to both cloud vertical distribution and near cloud-top microphysics. EOS Aqua, the local afternoon crossing satellite, carries in addition to MODIS the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), a conically scanning passive microwave (PMW) instrument with 12 channels between 6.9 and 89 GHz. While limited by revisit time from low-earth orbit, Aqua provides an ideal test bed for investigating high refresh-rate, geostationary-based blended PMW/optical-spectrum techniques related to improved cloud characterization and precipitation. As the need for improved subdaily quantitative precipitation analysis has grown in recent years, blended-satellite techniques have taken on added relevance. In this paper, we present an application of the MODIS/AMSR-E sensor combination related to potential improvements to the Naval Research Laboratory (NRL)-developed blended-satellite precipitation technique, involving the characterization of cirrus clouds. The presence of cirrus clouds above and nearby to both convective and stratiform precipitation imposes a limit to the utilization of longwave (>10 /spl mu/m wavelength) thermal infrared channels for precipitation techniques. The established "split window" technique involving the 11-12-/spl mu/m brightness temperature difference (BTD) perform well for thin cirrus. We demonstrate that the 1.38- /spl mu/m channel (and the 3.7-11-/spl mu/m BTD at night) on MODIS, when combined with additional channels, is capable of decoupling thin surrounding cirrus from thicker ice clouds. This information may be useful for screening thin cirrus that is often falsely interpreted as light precipitation. Radiative transfer simulations are used to demonstrate the theoretical basis for the selected multispectral channel combinations, and examples involving daytime and nighttime Aqua overpasses are presented.

The objective of this paper is to investigate how the complementarity between low earth orbit (LEO) microwave (MW) and geostationary earth orbit (GEO) infrared (IR) radiometric measurements can be exploited for satellite rainfall detection and estimation. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The basic idea behind the investigated statistical integration methods follows an established approach consisting in using the satellite MW-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne IR measurements on sufficiently limited subregions and time windows. The proposed methodologies are focused on new statistical approaches, namely the multivariate probability matching (MPM) and variance-constrained multiple regression (VMR). The MPM and VMR methods are rigorously formulated and systematically analyzed in terms of relative detection and estimation accuracy and computing efficiency. In order to demonstrate the potentiality of the proposed MW-IR combined rainfall algorithm (MICRA), three case studies are discussed, two on a global scale on November 1999 and 2000 and one over the Mediterranean area. A comprehensive set of statistical parameters for detection and estimation assessment is introduced to evaluate the error budget. For a comparative evaluation, the analysis of these case studies has been extended to similar techniques available in literature.

A series of validation statistics are presented from a operational global, minimum 3-hourly updating precipitation analysis. The technique was developed at the Naval Research Laboratory (NRL) for use in numerical weather prediction (NWP) model data assimilation and now casting operations. The technique is an adaptive statistical/hybrid technique which incorporates data from all current operational infrared-based geostationary satellites and from all (currently) available low-Earth orbiting microwave-based sensors, the Special Sensor Microwave Imager (SSMI), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Sounding Unit (AMSU-B). Knowledge of the rain estimation error statistics are needed in order to properly utilize precipitation observations in NWP variational assimilation techniques. Validation statistics are presented from comparisons with operationally supported raingauge networks in Australia and Korea in a two-dimensional fashion, where the spatial and temporal dimensions are each individually varied.

There exists a need for rapid updates of land and ocean-based precipitation within the atmospheric, oceanographic, and hydrologic communities. While presenting their own difficulties, satellite observations overcome many of the difficulties encountered in obtaining observations near coasts and over oceans. The current microwave-based Special Sensor Microwave Imagers (SSM/I) provide global coverage of rainfall from a polar-orbiting perspective. Conversely, operational geostationary imagers such as the Geostationary Operational Earth Satellites (GOES) provide rapid hourly (or less) updates in the infrared (IR) spectrum near 11 microns, which for optically thick clouds senses the emitted radiation from the upper cloud regions. This technique attempts to statistically fuse these two types of disjoint satellite data together in a real-time fashion for retrieval of instantaneous rain rate as well as accumulations at the geostationary update cycle. The technique works with any of the four current operational geostationary imagers (GOES-8, GOES-9, GMS 5 and Meteosat-6), and is easily adaptable to assimilate rain rates derived from the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI).

Passive microwave images of tropical cyclones worldwide can now be viewed on the Web in near real time. Instant access can be obtained to any “live” tropical cyclone. The images show rainband and eye structure beneath higher cloud decks and can be used to infer intensity and storm position. The images are available on the Web site of the U.S. Naval Research Laboratory's (NRL) Marine Meteorology Division (http://www.nrlmry.navy.mil/projects/sat_products.html, select “tropical cyclones”). Because of the composite nature of the images, users can view unfamiliar Special Sensor Microwave Imager (SSM/I) images juxtaposed with much more familiar visible and IR images (see Figure 1). This juxtaposition allows a number of interpretations of storm structure that would not be possible using information from a single sensor alone. The ability to detect storm eyes from microwave images, and therefore fix storm position, is significant since cyclone eyes are often obscured by cirrus canopies on visible and IR images. Besides microwave, visible and IR images of tropical cyclones, the Web site also has charts giving official U.S. forecasts of the storm tracks and intensities (example in Figure 2).