PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall from geosynchronous satellite longwave infared imagery (GOES-IR) at a resolution of 0.25° x 0.25° every half-hour. The accuracy of the rainfall product is improved by adaptively adjusting the network parameters using the instantaneous rain-rate estimates from the Tropical Rainfall Measurement Mission (TRMM) microwave imager (TMI product 2A12), and the random errors are further reduced by accumulation to a resolution of 1° x 1° daily. The authors' current GOES-IR-TRMM TMI based product, named PERSIANN-GT, was evaluated over the region 30°S-30°N, 90°E-30°W, which includes the tropical Pacific Ocean and parts of Asia, Australia, and the Americas. The resulting rain-rate estimates agree well with the National Climatic Data Center radar-gauge composite data over Florida and Texas (correlation coefficient p > 0.7). The product also compares well (p ~ 0.77-0.90) with the monthly World Meteorological Organization gauge measurements for 5° x 5° grid locations having high gauge densities. The PERSIANN-GT product was evaluated further by comparing it with current TRMM products (3A11, 3B31, 3B42, 3B43) over the entire study region. The estimates compare well with the TRMM 3B43 1° x 1° monthly product, but the PERSIANN-GT products indicate higher rainfall over the western Pacific Ocean when compared to the adjusted geosynchronous precipitation index-based TRMM 3B42 product.

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A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared (10.7 m) geostationary satellite imagery in estimating finescale (0.04Њ ϫ 0.04Њ every 30 min) rainfall distribution. This algorithm processes satellite cloud images into pixel rain rates by 1) separating cloud images into distinctive cloud patches; 2) extracting cloud features, including coldness, geometry, and texture; 3) clustering cloud patches into well-organized subgroups; and 4) calibrating cloud-top temperature and rainfall (T b -R) relationships for the classified cloud groups using gauge-corrected radar hourly rainfall data. Several cloud-patch categories with unique cloud-patch features and T b -R curves were identified and explained. Radar and gauge rainfall measurements were both used to evaluate the PERSIANN CCS rainfall estimates at a range of temporal (hourly and daily) and spatial (0.04Њ, 0.12Њ, and 0.25Њ) scales. Hourly evaluation shows that the correlation coefficient (CC) is 0.45 (0.59) at a 0.04Њ (0.25Њ) grid scale. The averaged CC of daily rainfall is 0.57 (0.63) for the winter (summer) season.

Multi-model ensemble strategy is a means to exploit the diversity of skillful predictions from different models. This paper studies the use of Bayesian model averaging (BMA) scheme to develop more skillful and reliable probabilistic hydrologic predictions from multiple competing predictions made by several hydrologic models. BMA is a statistical procedure that infers consensus predictions by weighing individual predictions based on their probabilistic likelihood measures, with the better performing predictions receiving higher weights than the worse performing ones. Furthermore, BMA provides a more reliable description of the total predictive uncertainty than the original ensemble, leading to a sharper and better calibrated probability density function (PDF) for the probabilistic predictions. In this study, a nine-member ensemble of hydrologic predictions was used to test and evaluate the BMA scheme. This ensemble was generated by calibrating three different hydrologic models using three distinct objective functions. These objective functions were chosen in a way that forces the models to capture certain aspects of the hydrograph well (e.g., peaks, mid-flows and low flows). Two sets of numerical experiments were carried out on three test basins in the US to explore the best way of using the BMA scheme. In the first set, a single set of BMA weights was computed to obtain BMA predictions, while the second set employed multiple sets of weights, with distinct sets corresponding to different flow intervals. In both sets, the streamflow values were transformed using Box-Cox transformation to ensure that the probability distribution of the prediction errors is approximately Gaussian. A split sample approach was used to obtain and validate the BMA predictions. The test results showed that BMA scheme has the advantage of generating more skillful and equally reliable probabilistic predictions than original ensemble. The performance of the expected BMA predictions in terms of daily root mean square error (DRMS) and daily absolute mean error (DABS) is generally superior to that of the best individual predictions. Furthermore, the BMA predictions employing multiple sets of weights are generally better than those using single set of weights.

[1] Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships. However, conventional ANN structures suffer from network training issues that significantly limit their widespread application. This paper presents a multivariate ANN procedure entitled self-organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive estimation of network structure/parameters and system outputs. More important, SOLO provides features that facilitate insight into the underlying processes, thereby extending its usefulness beyond forecast applications as a tool for scientific investigations. These characteristics are demonstrated using a classic rainfall-runoff forecasting problem. Various aspects of model performance are evaluated in comparison with other commonly used modeling approaches, including multilayer feedforward ANNs, linear time series modeling, and conceptual rainfall-runoff modeling.

[1] The effect of irrigation on regional climate has been studied over the years. However, in most studies, the model was usually set at coarse resolution, and the soil moisture was set to field capacity at each time step. We reinvestigated this issue over the Central Valley of California's agricultural area by: (1) using the regional climate model at different resolutions down to the finest resolution of 4 km for the most inner domain, covering California's Central Valley, the central coast, the Sierra Nevada Mountains, and water; (2) using a more realistic irrigation scheme in the model through the use of different allowable soil water depletion configurations; and (3) evaluating the simulated results against satellite and in situ observations available through the California Irrigation Management Information System (CIMIS). The simulation results with fine model resolution and with the more realistic irrigation scheme indicate that the surface meteorological fields are noticeably improved when compared with observations from the CIMIS network and Moderate Resolution Imaging Spectroradiometer data. Our results also indicate that irrigation has significant impacts on local meteorological fields by decreasing temperature by 3°-7°C and increasing relative humidity by 9-20%, depending on model resolutions and allowable soil water depletion configurations. More significantly, our results using the improved model show that the effects of irrigation on weather and climate do not extend very far into nonirrigated regions.

This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the best-calibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction.

Recent progress in satellite remote-sensing techniques for precipitation estimation, along with more accurate tropical rainfall measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) instruments, have made it possible to monitor tropical rainfall diurnal patterns and their intensities from satellite information. One year (August 1998-July 1999) of tropical rainfall estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system were used to produce monthly means of rainfall diurnal cycles at hourly and 1Њ ϫ 1Њ scales over a domain (30ЊS-30ЊN, 80ЊE-10ЊW) from the Americas across the Pacific Ocean to Australia and eastern Asia. The results demonstrate pronounced diurnal variability of tropical rainfall intensity at synoptic and regional scales. Seasonal signals of diurnal rainfall are presented over the large domain of the tropical Pacific Ocean, especially over the ITCZ and South Pacific convergence zone (SPCZ) and neighboring continents. The regional patterns of tropical rainfall diurnal cycles are specified in the Amazon, Mexico, the Caribbean Sea, Calcutta, Bay of Bengal, Malaysia, and northern Australia. Limited validations for the results include comparisons of 1) the PERSIANN-derived diurnal cycle of rainfall at Rondonia, Brazil, with that derived from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) radar data; 2) the PERSIANN diurnal cycle of rainfall over the western Pacific Ocean with that derived from the data of the optical rain gauges mounted on the TOGA-moored buoys; and 3) the monthly accumulations of rainfall samples from the orbital TMI and PR surface rainfall with the accumulations of concurrent PERSIANN estimates. These comparisons indicate that the PERSIANN-derived diurnal patterns at the selected resolutions produce estimates that are similar in magnitude and phase.