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Peter Bauer, George Ohring, Chris Kummerow, and Tom Auligne

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Chris Kidd, Toshihisa Matsui, Jiundar Chern, Karen Mohr, Chris Kummerow, and Dave Randel

Abstract

The estimation of precipitation across the globe from satellite sensors provides a key resource in the observation and understanding of our climate system. Estimates from all pertinent satellite observations are critical in providing the necessary temporal sampling. However, consistency in these estimates from instruments with different frequencies and resolutions is critical. This paper details the physically based retrieval scheme to estimate precipitation from cross-track (XT) passive microwave (PM) sensors on board the constellation satellites of the Global Precipitation Measurement (GPM) mission. Here the Goddard profiling algorithm (GPROF), a physically based Bayesian scheme developed for conically scanning (CS) sensors, is adapted for use with XT PM sensors. The present XT GPROF scheme utilizes a model-generated database to overcome issues encountered with an observational database as used by the CS scheme. The model database ensures greater consistency across meteorological regimes and surface types by providing a more comprehensive set of precipitation profiles. The database is corrected for bias against the CS database to ensure consistency in the final product. Statistical comparisons over western Europe and the United States show that the XT GPROF estimates are comparable with those from the CS scheme. Indeed, the XT estimates have higher correlations against surface radar data, while maintaining similar root-mean-square errors. Latitudinal profiles of precipitation show the XT estimates are generally comparable with the CS estimates, although in the southern midlatitudes the peak precipitation is shifted equatorward while over the Arctic large differences are seen between the XT and the CS retrievals.

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Robert F. Adler, Christian Kummerow, David Bolvin, Scott Curtis, and Chris Kidd

Abstract

Three years of Tropical Rainfall Measuring Mission (TRMM) monthly estimates of tropical surface rainfall are analyzed to document and understand the differences among the TRMM-based estimates and how these differences relate to the pre-TRMM estimates and current operational analyses. Variation among the TRMM estimates is shown to be considerably smaller than among a pre-TRMM collection of passive microwave-based products. Use of both passive and active microwave techniques in TRMM should lead to increased confidence in converged estimates.

Current TRMM estimates are shown to have a range of about 20% for the tropical ocean as a whole, with variations in heavily raining ocean areas of the Intertropical Convergence Zone (ITCZ) and South Pacific Convergence Zone (SPCZ) having differences over 30%. In midlatitude ocean areas the differences are smaller. Over land there is a distinct difference between the Tropics and midlatitude with a reversal between some of the products as to which tends to be relatively high or low. Comparisons of TRMM estimates with ocean atoll and land rain gauge information point to products that might have significant regional biases. The bias of the radar-based product is significantly low compared with atoll rain gauge data, while the passive microwave product is significantly high compared to rain gauge data in the deep Tropics.

The evolution of rainfall patterns during the recent change from intense El Niño to a long period of La Niña and then a gradual return to near neutral conditions is described using TRMM. The time history of integrated rainfall over the tropical oceans (and land) during this period differs among the passive and active microwave TRMM estimates.

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T. N. Krishnamurti, Sajani Surendran, D. W. Shin, Ricardo J. Correa-Torres, T. S. V. Vijaya Kumar, Eric Williford, Chris Kummerow, Robert F. Adler, Joanne Simpson, Ramesh Kakar, William S. Olson, and F. Joseph Turk

Abstract

This paper addresses real-time precipitation forecasts from a multianalysis–multimodel superensemble. The methodology for the construction of the superensemble forecasts follows previous recent publications on this topic. This study includes forecasts from multimodels of a number of global operational centers. A multianalysis component based on the Florida State University (FSU) global spectral model that utilizes TRMM and SSM/I datasets and a number of rain-rate algorithms is also included. The difference in the analysis arises from the use of these rain rates within physical initialization that produces distinct differences among these components in the divergence, heating, moisture, and rain-rate descriptions. A total of 11 models, of which 5 represent global operational models and 6 represent multianalysis forecasts from the FSU model initialized by different rain-rate algorithms, are included in the multianalysis–multimodel system studied here. In this paper, “multimodel” refers to different models whose forecasts are being assimilated for the construction of the superensemble. “Multianalysis” refers to different initial analysis contributing to forecasts from the same model. The term superensemble is being used here to denote the bias-corrected forecasts based on the products derived from the multimodel and the multianalysis. The training period is covered by nearly 120 forecast experiments prior to 1 January 2000 for each of the multimodels. These are all 3-day forecasts. The statistical bias of the models is determined from multiple linear regression of these forecasts against a “best” rainfall analysis field that is based on TRMM and SSM/I datasets and using the rain-rate algorithms recently developed at NASA Goddard Space Flight Center. This paper discusses the results of real-time rainfall forecasts based on this system. The main results of this study are that the multianalysis–multimodel superensemble has a much higher skill than the participating member models. The skill of this system is higher than those of the ensemble mean that assigns a weight of 1.0 to all including the poorer models and the ensemble mean of bias-removed individual models. The selective weights for the entire multianalysis–multimodel superensemble forecast system make it superior to individual models and the above mean representations. The skill of precipitation forecasts is addressed in several ways. The skill of the superensemble-based rain rates is shown to be higher than the following: (a) individual model's skills with and without physical initialization, (b) skill of the ensemble mean, and (c) skill of the ensemble mean of individually bias-removed models.

The equitable-threat scores at many thresholds of rain are also examined for the various models and noted that for days 1–3 of forecasts, the superensemble-based forecasts do have the highest skills. The training phase is a major component of the superensemble. Issues on optimizing the number of training days is addressed by examining training with days of high forecast skill versus training with low forecast skill, and training with the best available rain-rate datasets versus those from poor representations of rain. Finally the usefulness of superensemble forecasts of rain for providing possible guidance for flood events such as the one over Mozambique during February 2000 is shown.

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Richard A. Anthes, Mark W. Maier, Steve Ackerman, Robert Atlas, Lisa W. Callahan, Gerald Dittberner, Richard Edwing, Pamela G. Emch, Michael Ford, William B. Gail, Mitch Goldberg, Steve Goodman, Christian Kummerow, Terrance Onsager, Kevin Schrab, Chris Velden, Thomas Vonderhaar, and James G. Yoe

Abstract

Over a two-year period beginning in 2015, a panel of subject matter experts, the Space Platform Requirements Working Group (SPRWG), carried out an analysis and prioritization of different space-based observations supporting the National Oceanic and Atmospheric Administration (NOAA)’s operational services in the areas of weather, oceans, and space weather. NOAA leadership used the SPRWG analysis of space-based observational priorities in different mission areas, among other inputs, to inform the Multi-Attribute Utility Theory (MAUT)-based value model and the NOAA Satellite Observing Systems Architecture (NSOSA) study. The goal of the NSOSA study is to develop candidate satellite architectures for the era beginning in approximately 2030. The SPRWG analysis included a prioritized list of observational objectives together with the quantitative attributes of each objective at three levels of performance: a threshold level of minimal utility, an intermediate level that the community expects by 2030, and a maximum effective level, a level for which further improvements would not be cost effective. This process is believed to be unprecedented in the analysis of long-range plans for providing observations from space. This paper describes the process for developing the prioritized objectives and their attributes and how they were combined in the Environmental Data Record (EDR) Value Model (EVM). The EVM helped inform NOAA’s assessment of many potential architectures for its future observing system within the NSOSA study. However, neither the SPRWG nor its report represents official NOAA policy positions or decisions, and the responsibility for selecting and implementing the final architecture rests solely with NOAA senior leadership.

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Gail Skofronick-Jackson, Walter A. Petersen, Wesley Berg, Chris Kidd, Erich F. Stocker, Dalia B. Kirschbaum, Ramesh Kakar, Scott A. Braun, George J. Huffman, Toshio Iguchi, Pierre E. Kirstetter, Christian Kummerow, Robert Meneghini, Riko Oki, William S. Olson, Yukari N. Takayabu, Kinji Furukawa, and Thomas Wilheit

Abstract

Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. In 2014, the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) launched the Global Precipitation Measurement (GPM) Core Observatory (CO) spacecraft. The GPM CO carries the most advanced precipitation sensors currently in space including a dual-frequency precipitation radar provided by JAXA for measuring the three-dimensional structures of precipitation and a well-calibrated, multifrequency passive microwave radiometer that provides wide-swath precipitation data. The GPM CO was designed to measure rain rates from 0.2 to 110.0 mm h−1 and to detect moderate to intense snow events. The GPM CO serves as a reference for unifying the data from a constellation of partner satellites to provide next-generation, merged precipitation estimates globally and with high spatial and temporal resolutions. Through improved measurements of rain and snow, precipitation data from GPM provides new information such as details on precipitation structure and intensity; observations of hurricanes and typhoons as they transition from the tropics to the midlatitudes; data to advance near-real-time hazard assessment for floods, landslides, and droughts; inputs to improve weather and climate models; and insights into agricultural productivity, famine, and public health. Since launch, GPM teams have calibrated satellite instruments, refined precipitation retrieval algorithms, expanded science investigations, and processed and disseminated precipitation data for a range of applications. The current status of GPM, its ongoing science, and its future plans are presented.

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Wouter Dorigo, Stephan Dietrich, Filipe Aires, Luca Brocca, Sarah Carter, Jean-François Cretaux, David Dunkerley, Hiroyuki Enomoto, René Forsberg, Andreas Güntner, Michaela I. Hegglin, Rainer Hollmann, Dale F. Hurst, Johnny A. Johannessen, Chris Kummerow, Tong Lee, Kari Luojus, Ulrich Looser, Diego G. Miralles, Victor Pellet, Thomas Recknagel, Claudia Ruz Vargas, Udo Schneider, Philippe Schoeneich, Marc Schröder, Nigel Tapper, Valery Vuglinsky, Wolfgang Wagner, Lisan Yu, Luca Zappa, Michael Zemp, and Valentin Aich

Abstract

Life on Earth vitally depends on the availability of water. Human pressure on freshwater resources is increasing, as is human exposure to weather-related extremes (droughts, storms, floods) caused by climate change. Understanding these changes is pivotal for developing mitigation and adaptation strategies. The Global Climate Observing System (GCOS) defines a suite of Essential Climate Variables (ECVs), many related to the water cycle, required to systematically monitor the Earth's climate system. Since long-term observations of these ECVs are derived from different observation techniques, platforms, instruments, and retrieval algorithms, they often lack the accuracy, completeness, resolution, to consistently to characterize water cycle variability at multiple spatial and temporal scales.

Here, we review the capability of ground-based and remotely sensed observations of water cycle ECVs to consistently observe the hydrological cycle. We evaluate the relevant land, atmosphere, and ocean water storages and the fluxes between them, including anthropogenic water use. Particularly, we assess how well they close on multiple temporal and spatial scales. On this basis, we discuss gaps in observation systems and formulate guidelines for future water cycle observation strategies. We conclude that, while long-term water-cycle monitoring has greatly advanced in the past, many observational gaps still need to be overcome to close the water budget and enable a comprehensive and consistent assessment across scales. Trends in water cycle components can only be observed with great uncertainty, mainly due to insufficient length and homogeneity. An advanced closure of the water cycle requires improved model-data synthesis capabilities, particularly at regional to local scales.

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