Title: Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Abstract

Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. We report our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections inmore » terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product.« less

Authors:

Akbari Asanjan, Ata  ^[1];

• Search DOE PAGES for author "Akbari Asanjan, Ata"
• Search DOE PAGES for ORCID "0000-0002-8691-8212"

Yang, Tiantian  ^[2];

• Search DOE PAGES for author "Yang, Tiantian"
• Search DOE PAGES for ORCID "0000-0002-0148-396X"

Hsu, Kuolin ^[3]; Sorooshian, Soroosh  ^[1];

• Search DOE PAGES for author "Sorooshian, Soroosh"
• Search DOE PAGES for ORCID "0000-0001-7774-5113"

Lin, Junqiang ^[4]; Peng, Qidong ^[4]

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+ Show Author Affiliations

1. Univ. of California, Irvine, CA (United States)
2. Univ. of California, Irvine, CA (United States); Univ. of Oklahoma, Norman, OK (United States)
3. Univ. of California, Irvine, CA (United States); National Taiwan Ocean Univ., Keelung (Taiwan)
4. China Inst. of Water Resources and Hydropower Research, Beijing (China)

Publication Date:

Mon Oct 29 00:00:00 EDT 2018

Research Org.:

Univ. of California, Oakland, CA (United States)

Sponsoring Org.:

USDOE Office of International Affairs (IA); California Energy Commission; National Science Foundation (NSF); National Oceanic and Atmospheric Administration (NOAA); National Environmental Satellite, Data, and Information Service (NESDIS); National Climatic Data Center (NCDC); US Army Research Office (ARO)

OSTI Identifier:

1613787

Alternate Identifier(s):

OSTI ID: 1482741

Grant/Contract Number:  

IA0000018; 300-15-005; CCF-1331915; NA09NES4400006; NCSU CICS; W911NF-11-1-0422

Resource Type:

Accepted Manuscript

Journal Name:

Journal of Geophysical Research: Atmospheres

Additional Journal Information:

Journal Volume: 123; Journal Issue: 22; Journal ID: ISSN 2169-897X

Publisher:

American Geophysical Union

Country of Publication:

United States

Language:

English

Subject:

54 ENVIRONMENTAL SCIENCES; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; meteorology & atmospheric sciences; deep learning; LSTM; RNN; PERSIANN system; precipitation forecast; Rapid Refresh