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Phu Nguyen, Andrea Thorstensen, Soroosh Sorooshian, Kuolin Hsu, Amir Aghakouchak, Hamed Ashouri, Hoang Tran, and Dan Braithwaite

Abstract

Little dispute surrounds the observed global temperature changes over the past decades. As a result, there is widespread agreement that a corresponding response in the global hydrologic cycle must exist. However, exactly how such a response manifests remains unsettled. Here we use a unique recently developed long-term satellite-based record to assess changes in precipitation across spatial scales. We show that warm climate regions exhibit decreasing precipitation trends, while arid and polar climate regions show increasing trends. At the country scale, precipitation seems to have increased in 96 countries, and decreased in 104. We also explore precipitation changes over 237 global major basins. Our results show opposing trends at different scales, highlighting the importance of spatial scale in trend analysis. Furthermore, while the increasing global temperature trend is apparent in observations, the same cannot be said for the global precipitation trend according to the high-resolution dataset, PERSIANN-CDR, used in this study.

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Tran Tan Tien, Cong Thanh, Hoang Thanh Van, and Kieu Quoc Chanh

Abstract

In this study a method of retrieving optimum information of typhoon tracks in a multimodel ensemble of forecasts is explored. By treating the latitudes and longitudes of typhoon centers as components of two-dimensional track vectors and using the full ensemble mean as a first guess, it is shown that such a two-dimensional approach for the typhoon track forecast can be formulated as a multivariate optimization problem. Experiments with five nonhydrostatic primitive equation models during the 2004–08 typhoon seasons in the western North Pacific basin show some noticeable improvements in the forecasts of typhoon tracks in terms of the forecast errors and track smoothness with this multivariate approach. The advantages of the multivariate optimization approach are its portability and simplicity, which could make it easily adaptable to any operational typhoon forecast center that synthesizes typhoon track forecast products from different sources.

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Hoang Tran, Phu Nguyen, Mohammed Ombadi, Kuolin Hsu, Soroosh Sorooshian, and Konstantinos Andreadis

Abstract

Flood mapping from satellites provides large-scale observations of flood events, but cloud obstruction in satellite optical sensors limits its practical usability. In this study, we implemented the Variational Interpolation (VI) algorithm to remove clouds from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area (SCA) products. The VI algorithm estimated states of cloud-hindered pixels by constructing three-dimensional space–time surfaces based on assumptions of snow persistence. The resulting cloud-free flood maps, while maintaining the temporal resolution of the original MODIS product, showed an improvement of nearly 70% in average probability of detection (POD) (from 0.29 to 0.49) when validated with flood maps derived from Landsat-8 imagery. The second part of this study utilized the cloud-free flood maps for calibration of a hydrologic model to improve simulation of flood inundation maps. The results demonstrated the utility of the cloud-free maps, as simulated inundation maps had average POD, false alarm ratio (FAR), and Hanssen–Kuipers (HK) skill score of 0.87, 0.49, and 0.84, respectively, compared to POD, FAR, and HK of 0.70, 0.61, and 0.67 when original maps were used for calibration.

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Phu Nguyen, Soroosh Sorooshian, Andrea Thorstensen, Hoang Tran, Phat Huynh, Thanh Pham, Hamed Ashouri, Kuolin Hsu, Amir AghaKouchak, and Dan Braithwaite
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Phu Nguyen, Andrea Thorstensen, Soroosh Sorooshian, Qian Zhu, Hoang Tran, Hamed Ashouri, Chiyuan Miao, KuoLin Hsu, and Xiaogang Gao

Abstract

The purpose of this study is to use the PERSIANN–Climate Data Record (PERSIANN-CDR) dataset to evaluate the ability of 32 CMIP5 models in capturing the behavior of daily extreme precipitation estimates globally. The daily long-term historical global PERSIANN-CDR allows for a global investigation of eight precipitation indices that is unattainable with other datasets. Quantitative comparisons against CPC daily gauge; GPCP One-Degree Daily (GPCP1DD); and TRMM 3B42, version 7 (3B42V7), datasets show the credibility of PERSIANN-CDR to be used as the reference data for global evaluation of CMIP5 models. This work uniquely defines different study regions by partitioning global land areas into 25 groups based on continent and climate zone type. Results show that model performance in warm temperate and equatorial regions in capturing daily extreme precipitation behavior is largely mixed in terms of index RMSE and correlation, suggesting that these regions may benefit from weighted model averaging schemes or model selection as opposed to simple model averaging. The three driest climate regions (snow, polar, and arid) exhibit high correlations and low RMSE values when compared against PERSIANN-CDR estimates, with the exceptions of the cold regions showing an inability to capture the 95th and 99th percentile annual total precipitation characteristics. A comprehensive assessment of each model’s performance in each continent–climate zone defined group is provided as a guide for both model developers to target regions and processes that are not yet fully captured in certain climate types, and for climate model output users to be able to select the models and/or the study areas that may best fit their applications of interest.

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