PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed the estimation of rainfall geosynchronous satellite longwave infared imagery (GOES-IR) at a resolution 0.25° × every half-hour. The accuracy product is improved by adaptively adjusting network parameters instantaneous rain-rate estimates Tropical Rainfall Measurement Mission (TRMM) microwave imager (TMI 2A12), and random errors are further reduced...

A system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is under development at The University of Arizona. current core this an adaptive Network (ANN) model that estimates rainfall rates infrared satellite imagery and ground-surface information. was initially calibrated over the Japanese Islands remotely sensed data collected by Geostationary Meteorological Satellite (GMS) ground-based Automated Data Acquisition System (AMeDAS). then...

Abstract 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 infrared (10.7 μm) geostationary satellite imagery in estimating finescale (0.04° × 0.04° every 30 min) distribution. processes images into pixel rain rates by 1) separating distinctive patches; 2) extracting features, including...

Abstract Reservoirs are fundamental human‐built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers operators to understand how inflows changing under different hydrological climatic conditions enable forecast‐informed operations. Over the last decade, uses of Artificial Intelligence Data Mining [AI & DM] techniques assisting streamflow subseasonal seasonal forecasts have been...

Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when 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 procedure entitled self‐organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive...

Abstract This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), superensemble (MMSE), modified (M3SE), and weighted method (WAM). These were evaluated using results from Distributed Model Intercomparison Project (DMIP), an international project sponsored by National Weather Service (NWS) Office of Hydrologic Development (OHD). All obtained uncalibrated DMIP simulations compared against best-uncalibrated as well...

Abstract Despite the advantage of global coverage at high spatiotemporal resolutions, satellite remotely sensed precipitation estimates still suffer from insufficient accuracy that needs to be improved for weather, climate, and hydrologic applications. This paper presents a framework deep neural network (DNN) improves products, focusing on reducing bias false alarms. The state-of-the-art learning techniques developed in area machine specialize extracting structural information massive amount...

Satellite‐based remotely sensed data have the potential to provide hydrologically relevant information about spatially and temporally varying physical variables. A methodology for estimating such variables from multichannel is presented; approach based on a modified counterpropagation neural network (MCPN) both effective efficient at building complex nonlinear input‐output function mappings large amounts of data. An application high‐resolution estimation spatial temporal variation surface...

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 radar (PR) instruments, have made it possible to monitor diurnal patterns their intensities information. One year (August 1998–July 1999) of estimates Precipitation Estimation Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system were used produce...

[1] The effect of irrigation on regional climate has been studied over the years. However, in most studies, model was usually set at coarse resolution, and soil moisture to field capacity each time step. We reinvestigated this issue Central Valley California's agricultural area by: (1) using different resolutions down finest resolution 4 km for inner domain, covering Valley, central coast, Sierra Nevada Mountains, water; (2) a more realistic scheme through use allowable water depletion...

Abstract Numerical weather prediction models play a major role in forecasting, especially cases of extreme events. The Weather Research and Forecasting Model (WRF), among others, is extensively used for both research practical applications. Previous studies have highlighted the sensitivity this model to microphysics cumulus schemes. This study investigated performance WRF forecasting precipitation, hurricane track, landfall time using various A total 20 combinations schemes were used,...

More than ever in the history of science, researchers have at their fingertips an unprecedented wealth data from continuously orbiting satellites, weather monitoring instruments, ecological observatories, seismic stations, moored buoys, floats, and even model simulations forecasts. With just internet connection, scientists engineers can access atmospheric oceanic gridded time series observations, seismographs around world, minute‐by‐minute conditions near‐Earth space environment, other...

Abstract Compared to ground precipitation measurements, satellite-based estimation products have the advantage of global coverage and high spatiotemporal resolutions. However, accuracy is still insufficient serve many weather, climate, hydrologic applications at In this paper, authors develop a state-of-the-art deep learning framework for using bispectral satellite information, infrared (IR), water vapor (WV) channels. Specifically, two-stage from information designed, consisting an initial...

The importance of high‐resolution rainfall data to understanding the intricacies dynamics hydrological processes and describing them in a sophisticated accurate way has been increasingly realized. last decade witnessed number studies numerous approaches possibility transformation from one scale another, nearly unanimously pointing such possibility. However, an important limitation is that they treat process as realization stochastic process, therefore there seems be lack connection between...

Meteorological radar is a remote sensing system that provides rainfall estimations at high spatial and temporal resolutions. The radar-based intensities (R) are calculated from the observed reflectivities (Z). Often, rain gauge observations used in combination with data to find optimal parameters Z–R transformation equation. scale dependency of power-law when estimated reflectivity intensity explored herein. multiplicative (a) exponent (b) said be "scale dependent" if applying objective...

A method to improve the GOES Precipitation Index (GPI) technique by combining satellite microwave and infrared (IR) data is proposed tested. Using microwave-based rainfall estimates, method, termed Universally Adjusted GPI (UAGPI), modifies both parameters (i.e., IR brightness temperature threshold mean rain rate) minimize summation of estimation errors during sampling periods. With respect each grid, monthly estimates are obtained in a manner identical except for use optimized parameters....

Abstract In the development of a satellite-based precipitation product, two important aspects are sufficient information in satellite-input data and proper methodologies, which used to extract such connect it estimates. this study, effectiveness state-of-the-art deep learning (DL) approaches useful features from bispectral satellite information, infrared (IR), water vapor (WV) channels, produce rain/no-rain (R/NR) detection is explored. To verify models designed evaluated: first model,...

A comparative study of three snow models with different complexities was carried out to assess how a physically detailed model can improve modeling within general circulation models. The were (a) the U.S. Army Cold Regions Research and Engineering Laboratory Model (SNTHERM), which uses mixture theory simulate multiphase water energy transfer processes in layers; (b) simplified three-layer model, Snow–Atmosphere–Soil Transfer (SAST), includes only ice liquid-water phases;and (c) submodel...

An innovative algorithm, shuffled complexes with principal components analysis (SP‐UCI), is developed to overcome a critical deficiency of the complex evolution scheme: population degeneration. Population degeneration means that, during evolutionary search process, particles may degenerate into subspace full parameter space, thereby missing capacity fully exploring space. Being confined in even lead particle converge nonstationary points, which fatal malfunction. To this problem, SP‐UCI...

The agricultural sector is the largest consumer of water in California. impacts irrigation on local and/or regional weather and climate have been studied reported recent literature. However, because lack observations realistic schemes employed numerical models, most previous studies fall category sensitivity tests, focusing temperature variations. results being this paper are obtained by incorporating into MM5/Noah land surface model an method practiced California's farming sector. proposed...