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Ali Behrangi
,
Kuo-lin Hsu
,
Bisher Imam
,
Soroosh Sorooshian
, and
Robert J. Kuligowski

Abstract

Data from geosynchronous Earth-orbiting (GEO) satellites equipped with visible (VIS) and infrared (IR) scanners are commonly used in rain retrieval algorithms. These algorithms benefit from the high spatial and temporal resolution of GEO observations, either in stand-alone mode or in combination with higher-quality but less frequent microwave observations from low Earth-orbiting (LEO) satellites. In this paper, a neural network–based framework is presented to evaluate the utility of multispectral information in improving rain/no-rain (R/NR) detection. The algorithm uses the powerful classification features of the self-organizing feature map (SOFM), along with probability matching techniques to map single- or multispectral input space into R/NR maps. The framework was tested and validated using the 31 possible combinations of the five Geostationary Operational Environmental Satellite 12 (GOES-12) channels. An algorithm training and validation study was conducted over the conterminous United States during June–August 2006. The results indicate that during daytime, the visible channel (0.65 μm) can yield significant improvements in R/NR detection capabilities, especially when combined with any of the other four GOES-12 channels. Similarly, for nighttime detection the combination of two IR channels—particularly channels 3 (6.5 μm) and 4 (10.7 μm)—resulted in significant performance gain over any single IR channel. In both cases, however, using more than two channels resulted only in marginal improvements over two-channel combinations. Detailed examination of event-based images indicate that the proposed algorithm is capable of extracting information useful to screen no-rain pixels associated with cold, thin clouds and identifying rain areas under warm but rainy clouds. Both cases have been problematic areas for IR-only algorithms.

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Ali Behrangi
,
Kuo-lin Hsu
,
Bisher Imam
,
Soroosh Sorooshian
,
George J. Huffman
, and
Robert J. Kuligowski

Abstract

Visible and infrared data obtained from instruments onboard geostationary satellites have been extensively used for monitoring clouds and their evolution. The Advanced Baseline Imager (ABI) that will be launched onboard the Geostationary Operational Environmental Satellite-R (GOES-R) series in the near future will offer a larger range of spectral bands; hence, it will provide observations of cloud and rain systems at even finer spatial, temporal, and spectral resolutions than are possible with the current GOES. In this paper, a new method called Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks–Multispectral Analysis (PERSIANN-MSA) is proposed to evaluate the effect of using multispectral imagery on precipitation estimation. The proposed approach uses a self-organizing feature map (SOFM) to classify multidimensional input information, extracted from each grid box and corresponding textural features of multispectral bands. In addition, principal component analysis (PCA) is used to reduce the dimensionality to a few independent input features while preserving most of the variations of all input information. The above method is applied to estimate rainfall using multiple channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. In comparison to the use of a single thermal infrared channel, the analysis shows that using multispectral data has the potential to improve rain detection and estimation skills with an average of more than 50% gain in equitable threat score for rain/no-rain detection, and more than 20% gain in correlation coefficient associated with rain-rate estimation.

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Farid Ishak Boushaki
,
Kuo-Lin Hsu
,
Soroosh Sorooshian
,
Gi-Hyeon Park
,
Shayesteh Mahani
, and
Wei Shi

Abstract

Reliable precipitation measurement is a crucial component in hydrologic studies. Although satellite-based observation is able to provide spatial and temporal distribution of precipitation, the measurements tend to show systematic bias. This paper introduces a grid-based precipitation merging procedure in which satellite estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN–CCS) are adjusted based on the Climate Prediction Center (CPC) daily rain gauge analysis. To remove the bias, the hourly CCS estimates were spatially and temporally accumulated to the daily 1° × 1° scale, the resolution of CPC rain gauge analysis. The daily CCS bias was then downscaled to the hourly temporal scale to correct hourly CCS estimates. The bias corrected CCS estimates are called the adjusted CCS (CCSA) product. With the adjustment from the gauge measurement, CCSA data have been generated to provide more reliable high temporal/spatial-resolution precipitation estimates. In the case study, the CCSA precipitation estimates from the proposed approach are compared against ground-based measurements in high-density gauge networks located in the southwestern United States.

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Majid Mahrooghy
,
Valentine G. Anantharaj
,
Nicolas H. Younan
,
James Aanstoos
, and
Kuo-Lin Hsu

Abstract

By employing wavelet and selected features (WSF), median merging (MM), and selected curve-fitting (SCF) techniques, the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) has been improved. The PERSIANN-CCS methodology includes the following four main steps: 1) segmentation of satellite cloud images into cloud patches, 2) feature extraction, 3) classification of cloud patches, and 4) derivation of the temperature–rain-rate (T–R) relationship for every cluster. The enhancements help improve step 2 by employing WSF, and step 4 by employing MM and SCF. For the study area herein, the results show that the enhanced methodology improves the equitable threat score (ETS) of the daily and hourly rainfall estimates mostly in the winter and fall. The ETS percentage improvement is about 20% for the daily (10% for hourly) estimates in the winter, 10% for the daily (8% for hourly) estimates in the fall, and at most 5% for the daily estimates in the summer at some rainfall thresholds. In the winter and fall, the area bias is improved almost at all rainfall thresholds for daily and hourly estimates. However, no significant improvement is obtained in the spring, and the area bias in the summer is also greater than that of the implemented PERSIANN-CCS algorithm.

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Soroosh Sorooshian
,
Kuo-Lin Hsu
,
Xiaogang Gao
,
Hoshin V. Gupta
,
Bisher Imam
, and
Dan Braithwaite

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° × 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° × 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° × 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° × 5 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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Vesta Afzali Gorooh
,
Veljko Petković
,
Malarvizhi Arulraj
,
Phu Nguyen
,
Kuo-lin Hsu
,
Soroosh Sorooshian
, and
Ralph R. Ferraro

Abstract

Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth’s hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net–like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net–like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net–like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs’ importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology.

Open access
Koray K. Yilmaz
,
Terri S. Hogue
,
Kuo-lin Hsu
,
Soroosh Sorooshian
,
Hoshin V. Gupta
, and
Thorsten Wagener

Abstract

This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven operational basins of varying size and location in the southeastern United States. The analysis indicates that agreements between the datasets vary considerably from basin to basin and also temporally within the basins. The analysis also includes evaluation of MAPS in comparison with MAPG for use in flow forecasting with a lumped hydrologic model [Sacramento Soil Moisture Accounting Model (SAC-SMA)]. The latter evaluation investigates two different parameter sets, the first obtained using manual calibration on historical MAPG, and the second obtained using automatic calibration on both MAPS and MAPG, but over a shorter time period (23 months). Results indicate that the overall performance of the model simulations using MAPS depends on both the bias in the precipitation estimates and the size of the basins, with poorer performance in basins of smaller size (large bias between MAPG and MAPS) and better performance in larger basins (less bias between MAPG and MAPS). When using MAPS, calibration of the parameters significantly improved the model performance.

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Hamed Ashouri
,
Soroosh Sorooshian
,
Kuo-Lin Hsu
,
Michael G. Bosilovich
,
Jaechoul Lee
,
Michael F. Wehner
, and
Allison Collow

Abstract

This study evaluates the performance of NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979–2010. The Climate Prediction Center (CPC) U.S. Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRA tends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1) MERRA shows a spurious negative trend in Nebraska and Kansas, which is most likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over the Gulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. In addition, the increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.

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Hamed Ashouri
,
Kuo-Lin Hsu
,
Soroosh Sorooshian
,
Dan K. Braithwaite
,
Kenneth R. Knapp
,
L. Dewayne Cecil
,
Brian R. Nelson
, and
Olivier P. Prat

Abstract

A new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides daily and 0.25° rainfall estimates for the latitude band 60°S–60°N for the period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data. It is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5° monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset against available observations and satellite products are reported. The verification study over Hurricane Katrina (2005) shows that PERSIANN-CDR has good agreement with the stage IV radar data, noting that PERSIANN-CDR has more complete spatial coverage than the radar data. In addition, the comparison of PERSIANN-CDR against gauge observations during the 1986 Sydney flood in Australia reaffirms the capability of PERSIANN-CDR to provide reasonably accurate rainfall estimates. Moreover, the probability density function (PDF) of PERSIANN-CDR over the contiguous United States exhibits good agreement with the PDFs of the Climate Prediction Center (CPC) gridded gauge data and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) product. The results indicate high potential for using PERSIANN-CDR for long-term hydroclimate studies in regional and global scales.

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Efi Foufoula-Georgiou
,
Clement Guilloteau
,
Phu Nguyen
,
Amir Aghakouchak
,
Kuo-Lin Hsu
,
Antonio Busalacchi
,
F. Joseph Turk
,
Christa Peters-Lidard
,
Taikan Oki
,
Qingyun Duan
,
Witold Krajewski
,
Remko Uijlenhoet
,
Ana Barros
,
Pierre Kirstetter
,
William Logan
,
Terri Hogue
,
Hoshin Gupta
, and
Vincenzo Levizzani
Free access