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Chris Kidd, Ralph Ferraro, and Vincenzo Levizzani

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Chris Kidd, Erin Dawkins, and George Huffman

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Precipitation is an important component of the climate system, and the accurate representation of the diurnal rainfall cycle is a key test of model performance. Although the modeling of precipitation in the cooler midlatitudes has improved, in the tropics substantial errors still occur. Precipitation from the operational ECMWF forecast model is compared with satellite-derived products from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) and TRMM Precipitation Radar (PR) to assess the mean annual and seasonal diurnal rainfall cycles. The analysis encompasses the global tropics and subtropics (40°N–40°S) over a 7-yr period from 2004 to 2011. The primary aim of the paper is to evaluate the ability of an operational numerical model and satellite products to retrieve subdaily rainfall. It was found that during the first half of the analysis period the ECMWF model overestimated precipitation by up to 15% in the tropics, although after the implementation of a new convective parameterization in November 2007 this bias fell to about 4%. The ECMWF model poorly represented the diurnal cycle, simulating rainfall too early compared to the TMPA and TRMM PR products; the model simulation of precipitation was particularly poor over Indonesia. In addition, the model did not appear to simulate mountain-slope breezes well or adequately capture many of the characteristics of mesoscale convective systems. The work highlights areas for further study to improve the representation of subgrid-scale processes in parameterization schemes and improvements in model resolution. In particular, the proper representation of subdaily precipitation in models is critical for hydrological modeling and flow forecasting.

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Tim Bellerby, Martin Todd, Dom Kniveton, and Chris Kidd

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This paper describes the development of a satellite precipitation algorithm designed to generate rainfall estimates at high spatial and temporal resolutions using a combination of Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) data and multispectral Geostationary Operational Environmental Satellite (GOES) imagery. Coincident PR measurements were matched with four-band GOES image data to form the training dataset for a neural network. Statistical information derived from multiple GOES pixels was matched with each precipitation measurement to incorporate information on cloud texture and rates of change into the estimation process. The neural network was trained for a region of Brazil and used to produce half-hourly precipitation estimates for the periods 8–31 January and 10–25 February 1999 at a spatial resolution of 0.12 degrees. These products were validated using PR and gauge data. Instantaneous precipitation estimates demonstrated correlations of ∼0.47 with independent validation data, exceeding those of an optimized GOES Precipitation Index method locally calibrated using PR data. A combination of PR and GOES data thus may be used to generate precipitation estimates at high spatial and temporal resolutions with extensive spatial and temporal coverage, independent of any surface instrumentation.

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Victoria L. Sanderson, Chris Kidd, and Glenn R. McGregor

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This paper uses rainfall estimates retrieved from active and passive microwave data to investigate how spatially and temporally dependent algorithm biases affect the monitoring of the diurnal rainfall cycle. Microwave estimates used in this study are from the Tropical Rainfall Measuring Mission (TRMM) and include the precipitation radar (PR) near-surface (2A25), Goddard Profiling (GPROF) (2A12), and PR–TRMM Microwave Imager (TMI) (2B31) rain rates from the version 5 (v5) 3G68 product. A rainfall maximum is observed early evening over land, while oceans generally show a minimum in rainfall during the morning. Comparisons of annual and seasonal mean hourly rain rates and harmonics at both global and regional scales show significant differences between the algorithms. Relative and absolute biases over land vary according to the time of day. Clearly, these retrieval biases need accounting for, either in the physics of the algorithm or through the provision of accurate error estimates, to avoid erroneous climatic signals and the discrediting of satellite rainfall estimations.

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Francisco J. Tapiador, Chris Kidd, Vincenzo Levizzani, and Frank S. Marzano

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The purpose of this paper is to evaluate a new operational procedure to produce half-hourly rainfall estimates at 0.1° spatial resolution. Rainfall is estimated using a neural networks (NN)–based approach utilizing passive microwave (PMW) and infrared satellite measurements. Several neural networks are tested, from multilayer perceptron to adaptative resonance theory architectures. The NN analytical selection process is explained. Half- hourly rain gauge data over Andalusia, Spain, are used for validation purposes. Several interpolation procedures are tested to transform point to areal measurements, including the maximum entropy estimation method. Rainfall estimations are also compared with Geostationary Operational Environmental Satellite precipitation index and histogram-matching results. Half-hourly rainfall estimates give ∼0.6 correlations with PMW data (∼0.2 with gauge), and average correlations of up to 0.7 and 0.6 are obtained for 0.5° and 0.1° monthly accumulated estimates, respectively.

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Elizabeth E. Ebert, John E. Janowiak, and Chris Kidd

An increasing number of satellite-based rainfall products are now available in near–real time over the Internet to help meet the needs of weather forecasters and climate scientists, as well as a wide range of decision makers, including hydrologists, agriculturalists, emergency managers, and industrialists. Many of these satellite products are so newly developed that a comprehensive evaluation has not yet been undertaken. This article provides potential users of short-interval satellite rainfall estimates with information on the accuracy of such estimates. Since late 2002 the authors have been performing daily validation and intercomparisons of several operational satellite rainfall retrieval algorithms over Australia, the United States, and northwestern Europe. Short-range quantitative precipitation forecasts from four numerical weather prediction (NWP) models are also included for comparison.

Synthesis of four years of daily rainfall validation results shows that the satellite-derived estimates of precipitation occurrence, amount, and intensity are most accurate during the warm season and at lower latitudes, where the rainfall is primarily convective in nature. In contrast, the NWP models perform better than the satellite estimates during the cool season when non-convective precipitation is dominant. An optimal rain-monitoring strategy for remote regions might therefore judiciously combine information from both satellite and NWP models.

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Martin C. Todd, Chris Kidd, Dominic Kniveton, and Tim J. Bellerby

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There are numerous applications in climatology and hydrology where accurate information at scales smaller than the existing monthly/2.5° products would be invaluable. Here, a new microwave/infrared rainfall algorithm is introduced that combines satellite passive microwave (PMW) and infrared (IR) data to account for limitations in both data types. Rainfall estimates are produced at the high spatial resolution and temporal frequency of the IR data using rainfall information from the PMW data. An IRTb–rain rate relationship, variable in space and time, is derived from coincident observations of IRTb and PMW rain rate (accumulated over a calibration domain) using the probability matching method. The IRTb–rain rate relationship is then applied to IR imagery at full temporal resolution.

MIRA estimates of rainfall are evaluated over a range of spatial and temporal scales. Over the global Tropics and subtropics, optimum IR thresholds and IRTb–rain rate relationships are highly variable, reflecting the complexity of dominant cloud microphysical processes. As a result, MIRA shows sensitivity to these variations, resulting in potentially useful improvements in estimate accuracy at small scales in comparison to the Geostationary Operational Environmental Satellite Precipitation Index (GPI) and the PMW-calibrated Universally Adjusted GPI (UAGPI). Unlike some existing PMW/IR techniques, MIRA can successfully capture variability in rain rates at the smallest possible scales. At larger scales MIRA and UAGPI produce very similar improvements over the GPI. The results demonstrate the potential for a new high-resolution rainfall climatology from 1987 onward, using International Satellite Cloud Climatology Project DX and Special Sensor Microwave Imager data. For real-time regional or quasi-global applications, a temporally “rolling” calibration window is suggested.

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Chris Kidd, George Huffman, Viviana Maggioni, Philippe Chambon, and Riko Oki

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To address the need to map precipitation on a global scale a collection of satellites carrying passive microwave (PMW) radiometers has grown over the last 20 years to form a constellation of about 10-12 sensors at any one time. Over the same period, a broad range of science and user communities has become increasingly dependent on the precipitation products provided by these sensors. The constellation presently consists of both conical and cross-track scanning precipitation-capable multi-channel instruments, many of which are beyond their operational and design lifetime but continue to operate through the cooperation of the responsible agencies. The Group on Earth Observations and the Coordinating Group for Meteorological Satellites (CGMS), among other groups, have raised the issue of how a robust, future precipitation constellation should be constructed. The key issues of current and future requirements for the mapping of global precipitation from satellite sensors can be summarised as providing: 1) sufficiently fine spatial resolutions to capture precipitation-scale systems and reduce the beam-filling effects of the observations; 2) a wide channel diversity for each sensor to cover the range of precipitation types, characteristics and intensities observed across the globe; 3) an observation interval that provides temporal sampling commensurate with the variability of precipitation; and 4) precipitation radars and radiometers in low inclination orbit to provide a consistent calibration source, as demonstrated by the first two spaceborne radar/radiometer combinations on the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) mission Core Observatory (CO). These issues are critical in determining the direction of future constellation requirements, while preserving the continuity of the existing constellation necessary for long-term climate-scale studies.

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

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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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Chris Kidd, Dominic R. Kniveton, Martin C. Todd, and Tim J. Bellerby

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The development of a combined infrared and passive microwave satellite rainfall estimation technique is outlined. Infrared data from geostationary satellites are combined with polar-orbiting passive microwave estimates to provide 30-min rainfall estimates. Collocated infrared and passive microwave values are used to generate a database, which is accessed by a cumulative histogram matching approach to generate an infrared temperature–rain-rate relationship. The technique produces initial estimates at 30-min and 12-km resolution ready to be aggregated to the user requirements. A 4-month case study over Africa has been chosen to compare the results from this technique with those of some existing rainfall techniques. The results indicate that the technique outlined here has statistical scores that are similar to other infrared/passive microwave combined algorithms. Comparison with the Geostationary Operational Environmental Satellite (GOES) precipitation index shows that while these algorithms result in lower correlation scores, areal statistics are significantly better than either the infrared or passive microwave techniques alone.

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