Search Menu
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information
Search Scholarly Publications

Title: Evaluation of Subseasonal-to-Seasonal (S2S) precipitation forecast from the North American Multi-Model ensemble phase II (NMME-2) over the contiguous U.S.

Abstract

The second phase of the North America Multi-Model Ensemble (NMME-2) provides globally available Subseasonal-to-Seasonal (S2S) precipitation forecasts with a daily resolution. The S2S precipitation forecasts are getting increasing attention for their potentials in providing hydrometeorological forcing information for water resources planning at an extended range. However, the forecast skills of many existing S2S forecast products will significantly decrease when the lead time increases, hindering their applicability for watershed-scale hydrologic modeling. Therefore, forecast validation and large-scale evaluation are of great importance for water resources planning and hydrological applications. In this study, we comprehensively evaluate the S2S precipitation forecasts from the NMME-2 dataset over the contiguous United States (CONUS) and during the study period from 1982 to 2011. Three aspects of precipitation forecast capabilities are compared and analyzed: bias, skill scores, and the ability to predict extreme precipitation events. The Parameter-elevation Regressions on Independent Slopes Model (PRISM) is used as ground truth reference. Differs from other regional forecast validation study, we further examined and analyzed the dependences of NMME-2 precipitation forecast skills according to different seasonality, geographical locations, and lead times. Results show that the forecast biases are not sensitive to lead times but are seasonally dependent of all NMME-2 models. Overestimationsmore » are found in the Western U.S. in cooler seasons while underestimations are observed in the central regions of the U.S. in warmer seasons. The forecast skill of all individual NMME-2 models generally decreases as increases of lead times. The simple model averaging (SMA) of five NMME-2 models demonstrates a higher forecast skill than any individual NMME-2 models. Spatially, the highest forecast skill scores are observed at coastal areas in the Western U.S. with an one-week lead time. As compared to the historical resampled forecasts, NMME-2 also shows better performance in predicting extreme precipitation events above 99% percentiles and below 1% percentiles with higher probability of detections and lower false alarm ratios. Finally, the obtained results suggest the great potentials of NMME-2 precipitation forecasts in assisting ensemble hydrologic forecasts at the S2S scale over the CONUS.« less

Authors:
 [1];  [1];  [1];  [1];  [2]
+ Show Author Affiliations
  1. Univ. of Oklahoma, Norman, OK (United States)
  2. Southeast Univ., Nanjing (China)
Publication Date:
Research Org.:
Univ. of California, Oakland, CA (United States)
Sponsoring Org.:
USDOE Office of International Affairs (IA); National Science Foundation (NSF); Natural Science Foundation of Jiangsu Province, China
OSTI Identifier:
1977302
Alternate Identifier(s):
OSTI ID: 1862562
Grant/Contract Number:  
IA0000018; OIA-1946093; EPSCoR-2020-3; NSF1802872; BK20180403
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Hydrology
Additional Journal Information:
Journal Volume: 603; Journal Issue: PB; Journal ID: ISSN 0022-1694
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Engineering; Geology; Water resources; NMME-2; Subseasonal-to-seasonal precipitation forecast; CONUS; Forecast validation; Forecast bias; Extreme precipitation

Citation Formats

Zhang, Lujun, Kim, Taereem, Yang, Tiantian, Hong, Yang, and Zhu, Qian. Evaluation of Subseasonal-to-Seasonal (S2S) precipitation forecast from the North American Multi-Model ensemble phase II (NMME-2) over the contiguous U.S.. United States: N. p., 2021. Web. doi:10.1016/j.jhydrol.2021.127058.
Copy to clipboard
Zhang, Lujun, Kim, Taereem, Yang, Tiantian, Hong, Yang, & Zhu, Qian. Evaluation of Subseasonal-to-Seasonal (S2S) precipitation forecast from the North American Multi-Model ensemble phase II (NMME-2) over the contiguous U.S.. United States. https://doi.org/10.1016/j.jhydrol.2021.127058
Copy to clipboard
Zhang, Lujun, Kim, Taereem, Yang, Tiantian, Hong, Yang, and Zhu, Qian. Fri . "Evaluation of Subseasonal-to-Seasonal (S2S) precipitation forecast from the North American Multi-Model ensemble phase II (NMME-2) over the contiguous U.S.". United States. https://doi.org/10.1016/j.jhydrol.2021.127058. https://www.osti.gov/servlets/purl/1977302.
Copy to clipboard
@article{osti_1977302,
title = {Evaluation of Subseasonal-to-Seasonal (S2S) precipitation forecast from the North American Multi-Model ensemble phase II (NMME-2) over the contiguous U.S.},
author = {Zhang, Lujun and Kim, Taereem and Yang, Tiantian and Hong, Yang and Zhu, Qian},
abstractNote = {The second phase of the North America Multi-Model Ensemble (NMME-2) provides globally available Subseasonal-to-Seasonal (S2S) precipitation forecasts with a daily resolution. The S2S precipitation forecasts are getting increasing attention for their potentials in providing hydrometeorological forcing information for water resources planning at an extended range. However, the forecast skills of many existing S2S forecast products will significantly decrease when the lead time increases, hindering their applicability for watershed-scale hydrologic modeling. Therefore, forecast validation and large-scale evaluation are of great importance for water resources planning and hydrological applications. In this study, we comprehensively evaluate the S2S precipitation forecasts from the NMME-2 dataset over the contiguous United States (CONUS) and during the study period from 1982 to 2011. Three aspects of precipitation forecast capabilities are compared and analyzed: bias, skill scores, and the ability to predict extreme precipitation events. The Parameter-elevation Regressions on Independent Slopes Model (PRISM) is used as ground truth reference. Differs from other regional forecast validation study, we further examined and analyzed the dependences of NMME-2 precipitation forecast skills according to different seasonality, geographical locations, and lead times. Results show that the forecast biases are not sensitive to lead times but are seasonally dependent of all NMME-2 models. Overestimations are found in the Western U.S. in cooler seasons while underestimations are observed in the central regions of the U.S. in warmer seasons. The forecast skill of all individual NMME-2 models generally decreases as increases of lead times. The simple model averaging (SMA) of five NMME-2 models demonstrates a higher forecast skill than any individual NMME-2 models. Spatially, the highest forecast skill scores are observed at coastal areas in the Western U.S. with an one-week lead time. As compared to the historical resampled forecasts, NMME-2 also shows better performance in predicting extreme precipitation events above 99% percentiles and below 1% percentiles with higher probability of detections and lower false alarm ratios. Finally, the obtained results suggest the great potentials of NMME-2 precipitation forecasts in assisting ensemble hydrologic forecasts at the S2S scale over the CONUS.},
doi = {10.1016/j.jhydrol.2021.127058},
journal = {Journal of Hydrology},
number = PB,
volume = 603,
place = {United States},
year = {Fri Oct 15 00:00:00 EDT 2021},
month = {Fri Oct 15 00:00:00 EDT 2021}
}
Copy to clipboard
Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.jhydrol.2021.127058
Copyright Statement
Other availability
Search WorldCat Search WorldCat to find libraries that may hold this journal
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Works referenced in this record:

Short‐Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks
journal, November 2018

  • Akbari Asanjan, Ata; Yang, Tiantian; Hsu, Kuolin
  • Journal of Geophysical Research: Atmospheres, Vol. 123, Issue 22
  • DOI: 10.1029/2018JD028375

The Subseasonal to Seasonal (S2S) Prediction Project Database
journal, January 2017

  • Vitart, F.; Ardilouze, C.; Bonet, A.
  • Bulletin of the American Meteorological Society, Vol. 98, Issue 1
  • DOI: 10.1175/BAMS-D-16-0017.1

Development of a European Multimodel Ensemble System for Seasonal-To-Interannual Prediction (Demeter)
journal, June 2004

  • Palmer, T. N.; Alessandri, A.; Andersen, U.
  • Bulletin of the American Meteorological Society, Vol. 85, Issue 6
  • DOI: 10.1175/BAMS-85-6-853

A large-scale, high-resolution hydrological model parameter data set for climate change impact assessment for the conterminous US
journal, January 2014

  • Oubeidillah, A. A.; Kao, S. -C.; Ashfaq, M.
  • Hydrology and Earth System Sciences, Vol. 18, Issue 1
  • DOI: 10.5194/hess-18-67-2014

Toward a Dynamical Foundation for Organized Convection Parameterization in GCMs
journal, December 2019

The sub-seasonal to seasonal prediction project (S2S) and the prediction of extreme events
journal, March 2018

  • Vitart, Frédéric; Robertson, Andrew W.
  • npj Climate and Atmospheric Science, Vol. 1, Issue 1
  • DOI: 10.1038/s41612-018-0013-0

A Test Bed for New Seasonal Hydrologic Forecasting Approaches in the Western United States
journal, December 2006

  • Wood, Andrew W.; Lettenmaier, Dennis P.
  • Bulletin of the American Meteorological Society, Vol. 87, Issue 12
  • DOI: 10.1175/BAMS-87-12-1699

Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information: RESERVOIR INFLOW FORECASTS
journal, April 2017

  • Yang, Tiantian; Asanjan, Ata Akbari; Welles, Edwin
  • Water Resources Research, Vol. 53, Issue 4
  • DOI: 10.1002/2017WR020482

Precipitation forecast skill of numerical weather prediction models and radar nowcasts: PRECIPITATION FORECAST SKILL
journal, July 2005

  • Lin, Charles; Vasić, Slavko; Kilambi, Alamelu
  • Geophysical Research Letters, Vol. 32, Issue 14
  • DOI: 10.1029/2005GL023451

Use of NWP for Nowcasting Convective Precipitation: Recent Progress and Challenges
journal, March 2014

  • Sun, Juanzhen; Xue, Ming; Wilson, James W.
  • Bulletin of the American Meteorological Society, Vol. 95, Issue 3
  • DOI: 10.1175/BAMS-D-11-00263.1

Global Microwave Satellite Observations of Sea Surface Temperature for Numerical Weather Prediction and Climate Research
journal, August 2005

  • Chelton, Dudley B.; Wentz, Frank J.
  • Bulletin of the American Meteorological Society, Vol. 86, Issue 8
  • DOI: 10.1175/BAMS-86-8-1097

An analysis of prediction skill of monthly mean climate variability
journal, August 2010

Evolution of the North American Multi‐Model Ensemble
journal, May 2020

  • Becker, Emily; Kirtman, Ben P.; Pegion, Kathy
  • Geophysical Research Letters, Vol. 47, Issue 9
  • DOI: 10.1029/2020GL087408

Advances in weather prediction
journal, January 2019

Verification of Land–Atmosphere Coupling in Forecast Models, Reanalyses, and Land Surface Models Using Flux Site Observations
journal, February 2018

  • Dirmeyer, Paul A.; Chen, Liang; Wu, Jiexia
  • Journal of Hydrometeorology, Vol. 19, Issue 2
  • DOI: 10.1175/JHM-D-17-0152.1

Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose
journal, January 2013

  • Shrestha, D. L.; Robertson, D. E.; Wang, Q. J.
  • Hydrology and Earth System Sciences, Vol. 17, Issue 5
  • DOI: 10.5194/hess-17-1913-2013

Advanced Concepts on Remote Sensing of Precipitation at Multiple Scales
journal, October 2011

  • Sorooshian, Soroosh; AghaKouchak, Amir; Arkin, Phillip
  • Bulletin of the American Meteorological Society, Vol. 92, Issue 10
  • DOI: 10.1175/2011BAMS3158.1

Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning
journal, January 2019

The Contribution of Mesoscale Convective Weather Systems to the Warm-Season Precipitation in the United States
journal, October 1986

Land surface impacts on subseasonal and seasonal predictability: LAND IMPACTS SUBSEASONAL PREDICTABILITY
journal, December 2011

  • Guo, Zhichang; Dirmeyer, Paul A.; DelSole, Tim
  • Geophysical Research Letters, Vol. 38, Issue 24
  • DOI: 10.1029/2011GL049945

An intercomparison of approaches for improving operational seasonal streamflow forecasts
journal, January 2017

  • Mendoza, Pablo A.; Wood, Andrew W.; Clark, Elizabeth
  • Hydrology and Earth System Sciences, Vol. 21, Issue 7
  • DOI: 10.5194/hess-21-3915-2017

Influence of snow and soil moisture initialization on sub-seasonal predictability and forecast skill in boreal spring
journal, September 2015

  • Thomas, Jaison Ambadan; Berg, Aaron A.; Merryfield, William J.
  • Climate Dynamics, Vol. 47, Issue 1-2
  • DOI: 10.1007/s00382-015-2821-9

PRISM vs . CFSR Precipitation Data Effects on Calibration and Validation of SWAT Models
journal, December 2016

  • Radcliffe, D. E.; Mukundan, R.
  • JAWRA Journal of the American Water Resources Association, Vol. 53, Issue 1
  • DOI: 10.1111/1752-1688.12484

Forecast Problems in the Western Region of the National Weather Service: An Overview
journal, June 1993

Variations in global temperature and precipitation for the period of 1948 to 2010
journal, May 2014

  • Sun, Qiaohong; Kong, Dongxian; Miao, Chiyuan
  • Environmental Monitoring and Assessment, Vol. 186, Issue 9
  • DOI: 10.1007/s10661-014-3811-9

Measuring forecast skill: is it real skill or is it the varying climatology?
journal, October 2006

  • Hamill, Thomas M.; Juras, Josip
  • Quarterly Journal of the Royal Meteorological Society, Vol. 132, Issue 621C
  • DOI: 10.1256/qj.06.25

Multimodel Ensemble Forecasts of Precipitation Based on an Object-Based Diagnostic Evaluation
journal, June 2020

Potential applications of subseasonal-to-seasonal (S2S) predictions: Potential applications of subseasonal-to-seasonal (S2S) predictions
journal, April 2017

  • White, Christopher J.; Carlsen, Henrik; Robertson, Andrew W.
  • Meteorological Applications, Vol. 24, Issue 3
  • DOI: 10.1002/met.1654

Influence of bias correction of meteorological and streamflow forecast on hydrological prediction in India
journal, January 2021

  • Tiwari, Amar Deep; Mukhopadhyay, Parthasarathi; Mishra, Vimal
  • Journal of Hydrometeorology
  • DOI: 10.1175/JHM-D-20-0235.1

An effective post-processing of the North American multi-model ensemble (NMME) precipitation forecasts over the continental US
journal, October 2017

  • Khajehei, Sepideh; Ahmadalipour, Ali; Moradkhani, Hamid
  • Climate Dynamics, Vol. 51, Issue 1-2
  • DOI: 10.1007/s00382-017-3934-0

The economic value of ensemble forecasts as a tool for risk assessment: From days to decades
journal, April 2002

  • Palmer, T. N.
  • Quarterly Journal of the Royal Meteorological Society, Vol. 128, Issue 581
  • DOI: 10.1256/0035900021643593

Statistical processing of forecasts for hydrological ensemble prediction: a comparative study of different bias correction strategies
journal, January 2012

  • Zalachori, I.; Ramos, M. -H.; Garçon, R.
  • Advances in Science and Research, Vol. 8, Issue 1
  • DOI: 10.5194/asr-8-135-2012

Skill, Correction, and Downscaling of GCM-Simulated Precipitation
journal, June 2012

Model Uncertainty in a Mesoscale Ensemble Prediction System: Stochastic versus Multiphysics Representations
journal, June 2011

  • Berner, J.; Ha, S. -Y.; Hacker, J. P.
  • Monthly Weather Review, Vol. 139, Issue 6
  • DOI: 10.1175/2010MWR3595.1

Incorporation of seasonal climate forecasts in the ensemble streamflow prediction system
journal, May 2010

Temperature trends and prediction skill in NMME seasonal forecasts
journal, April 2017

Review of trend analysis and climate change projections of extreme precipitation and floods in Europe
journal, November 2014

Predictability and Forecast Skill in NMME
journal, August 2014

Extended Streamflow Forecasting Using NWSRFS
journal, April 1985

Weighting of NMME temperature and precipitation forecasts across Europe
journal, September 2017

Evaluation of NMME temperature and precipitation bias and forecast skill for South Asia
journal, August 2017

  • Cash, Benjamin A.; Manganello, Julia V.; Kinter, James L.
  • Climate Dynamics, Vol. 53, Issue 12
  • DOI: 10.1007/s00382-017-3841-4

Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue
journal, March 2013

Prediction of vegetation anomalies to improve food security and water management in India: NDVI FORECAST IN INDIA
journal, July 2015

  • Asoka, Akarsh; Mishra, Vimal
  • Geophysical Research Letters, Vol. 42, Issue 13
  • DOI: 10.1002/2015GL063991

The quiet revolution of numerical weather prediction
journal, September 2015

  • Bauer, Peter; Thorpe, Alan; Brunet, Gilbert
  • Nature, Vol. 525, Issue 7567
  • DOI: 10.1038/nature14956

Atmospheric initial conditions and the predictability of the Arctic Oscillation: Predictability of the AO
journal, February 2015

  • Stockdale, Timothy N.; Molteni, Franco; Ferranti, Laura
  • Geophysical Research Letters, Vol. 42, Issue 4
  • DOI: 10.1002/2014GL062681

Development of Climate and Earth System Models in China: Past Achievements and New CMIP6 Results
journal, February 2020

  • Zhou, Tianjun; Chen, Ziming; Zou, Liwei
  • Journal of Meteorological Research, Vol. 34, Issue 1
  • DOI: 10.1007/s13351-020-9164-0

Evaluation and Bias Correction of S2S Precipitation for Hydrological Extremes
journal, September 2019

A Nonparametric Postprocessor for Bias Correction of Hydrometeorological and Hydrologic Ensemble Forecasts
journal, June 2010

  • Brown, James D.; Seo, Dong-Jun
  • Journal of Hydrometeorology, Vol. 11, Issue 3
  • DOI: 10.1175/2009JHM1188.1

Local Relationships between United States West Coast Precipitation and Monthly Mean Circulation Parameters
journal, June 1984

The Relationship Between Extratropical Cyclone Strength and Atmospheric River Intensity and Position
journal, February 2019

  • Zhang, Zhenhai; Ralph, F. Martin; Zheng, Minghua
  • Geophysical Research Letters, Vol. 46, Issue 3
  • DOI: 10.1029/2018GL079071

High-resolution ensemble projections of near-term regional climate over the continental United States: CLIMATE PROJECTIONS OVER THE U.S.
journal, September 2016

  • Ashfaq, Moetasim; Rastogi, Deeksha; Mei, Rui
  • Journal of Geophysical Research: Atmospheres, Vol. 121, Issue 17
  • DOI: 10.1002/2016JD025285

Simulating Hydropower Discharge using Multiple Decision Tree Methods and a Dynamical Model Merging Technique
journal, February 2020

The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction
journal, April 2014

  • Kirtman, Ben P.; Min, Dughong; Infanti, Johnna M.
  • Bulletin of the American Meteorological Society, Vol. 95, Issue 4
  • DOI: 10.1175/BAMS-D-12-00050.1

Skillful All-Season S2S Prediction of U.S. Precipitation Using the MJO and QBO
journal, October 2020

  • Nardi, Kyle M.; Baggett, Cory F.; Barnes, Elizabeth A.
  • Weather and Forecasting, Vol. 35, Issue 5
  • DOI: 10.1175/WAF-D-19-0232.1

Storm Morphology and Rainfall Characteristics of TRMM Precipitation Features
journal, October 2006

  • Nesbitt, Stephen W.; Cifelli, Robert; Rutledge, Steven A.
  • Monthly Weather Review, Vol. 134, Issue 10
  • DOI: 10.1175/MWR3200.1

Evaluation of the PERSIANN-CDR Daily Rainfall Estimates in Capturing the Behavior of Extreme Precipitation Events over China
journal, June 2015

  • Miao, Chiyuan; Ashouri, Hamed; Hsu, Kuo-Lin
  • Journal of Hydrometeorology, Vol. 16, Issue 3
  • DOI: 10.1175/JHM-D-14-0174.1

Summertime daily precipitation statistics over East China in CFSv2
journal, February 2020

Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations
journal, August 2016

Improved water allocation utilizing probabilistic climate forecasts: Short-term water contracts in a risk management framework: WATER CONTRACTS FOR PROBABILISTIC FORECASTS
journal, November 2009

  • Sankarasubramanian, A.; Lall, Upmanu; Souza Filho, Francisco Assis
  • Water Resources Research, Vol. 45, Issue 11
  • DOI: 10.1029/2009WR007821

Developing Subseasonal to Seasonal Climate Forecast Products for Hydrology and Water Management
journal, December 2018

  • Baker, Sarah A.; Wood, Andrew W.; Rajagopalan, Balaji
  • JAWRA Journal of the American Water Resources Association, Vol. 55, Issue 4
  • DOI: 10.1111/1752-1688.12746

Assessing the Representation of Synoptic Variability Associated With California Extreme Precipitation in CMIP6 Models
journal, March 2021

  • Norris, Jesse; Hall, Alex; Chen, Di
  • Journal of Geophysical Research: Atmospheres, Vol. 126, Issue 6
  • DOI: 10.1029/2020JD033938

Skill Scores and Correlation Coefficients in Model Verification
journal, March 1989

Improving Precipitation Estimation Using Convolutional Neural Network
journal, March 2019

  • Pan, Baoxiang; Hsu, Kuolin; AghaKouchak, Amir
  • Water Resources Research, Vol. 55, Issue 3
  • DOI: 10.1029/2018WR024090

Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network
journal, May 2019

  • Miao, Qinghua; Pan, Baoxiang; Wang, Hao
  • Water, Vol. 11, Issue 5
  • DOI: 10.3390/w11050977

Precipitation Prediction Skill for the West Coast United States: From Short to Extended Range
journal, December 2018

Multi-criterion model ensemble of CMIP5 surface air temperature over China
journal, May 2017

  • Yang, Tiantian; Tao, Yumeng; Li, Jingjing
  • Theoretical and Applied Climatology, Vol. 132, Issue 3-4
  • DOI: 10.1007/s00704-017-2143-4

Seamless Precipitation Prediction Skill in the Tropics and Extratropics from a Global Model
journal, March 2014

  • Zhu, Hongyan; Wheeler, Matthew C.; Sobel, Adam H.
  • Monthly Weather Review, Vol. 142, Issue 4
  • DOI: 10.1175/MWR-D-13-00222.1

Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging
journal, September 2007

  • Sloughter, J. Mc Lean; Raftery, Adrian E.; Gneiting, Tilmann
  • Monthly Weather Review, Vol. 135, Issue 9
  • DOI: 10.1175/MWR3441.1
    Sort by:
    [ × clear filter / sort ]

    Similar Records in DOE PAGES and OSTI.GOV collections: