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Chris Kidd, Ralph Ferraro, and Vincenzo Levizzani

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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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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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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, 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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Catherine L. Muller, Andy Baker, Ian J. Fairchild, Chris Kidd, and Ian Boomer

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Annual, monthly, and daily analyses of stable isotopes in precipitation are commonly made worldwide, yet only a few studies have explored the variations occurring on short time scales within individual precipitation events, particularly at midlatitude locations. This study examines hydrogen isotope data from sequential, intra-event samples from 16 precipitation events during different seasons and a range of synoptic conditions over an 18-month period in Birmingham, United Kingdom. Precipitation events were observed simultaneously using a vertically pointing micro rain radar (MRR), which, for the first time at a midlatitude location, allowed high-resolution examination of the microphysical characteristics (e.g., rain rate, fall velocity, and drop size distributions) that may influence the local isotopic composition of rainwater. The range in the hydrogen isotope ratio (δD, where D refers to deuterium) in 242 samples during 16 events was from −87.0‰ to +9.2‰, while the largest variation observed in a single event was 55.4‰. In contrast to previous work, the results indicate that some midlatitude precipitation events do indeed show significant intra-event trends that are strongly influenced by precipitation processes and parameters such as rain rate, melting-level height, and droplet sizes. Inverse relationships between rain rate and isotopic composition are observed, representing an example of a local type of “amount effect,” a still poorly understood process occurring at different scales. For these particular events, the mean δ value may therefore not provide all the relevant information. This work has significance for the testing and development of isotope-enabled cloud-resolving models and land surface models at higher resolutions, and it provides improved insights into a range of environmental processes that are influenced by subsampled precipitation events.

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

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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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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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