Seasonal Prediction of Summer Precipitation over East Africa Using NUIST-CFS1.0

Abstract A new scheme is developed to improve the seasonal prediction of summer precipitation in the East Asian and western Pacific region. The scheme is applied to the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) results. The new scheme is designed to consider both model predictions and observed spatial patterns of historical “analog years.” In this paper, the anomaly pattern correlation coefficient (ACC) between the prediction and the observation, as well as the root-mean-square error, is used to measure the prediction skill. For the prediction of summer precipitation in East Asia and the western Pacific (0°–40°N, 80°–130°E), the prediction skill for the six model ensemble hindcasts for the years of 1979–2001 was increased to 0.22 by using the new scheme from 0.12 for the original scheme. All models were initiated in May and were composed of nine member predictions, and all showed improvement when applying the new scheme. The skill levels of the predictions for the six models increased from 0.08, 0.08, 0.01, 0.14, −0.07, and 0.07 for the original scheme to 0.11, 0.14, 0.10, 0.22, 0.04, and 0.13, respectively, for the new scheme.

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Dynamical seasonal prediction of Southern African summer precipitation

Climate Dynamics ◽

10.1007/s00382-013-1923-5 ◽

2013 ◽

Vol 42 (11-12) ◽

pp. 3357-3374 ◽

Cited By ~ 15

Author(s):

Chaoxia Yuan ◽

Tomoki Tozuka ◽

Willem A. Landman ◽

Toshio Yamagata

Keyword(s):

Summer Precipitation ◽

Seasonal Prediction ◽

Dynamical Seasonal Prediction ◽

Southern African Summer Precipitation

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U.S. Summer Precipitation and Temperature Patterns Following the Peak Phase of El Niño

Journal of Climate ◽

10.1175/jcli-d-11-00660.1 ◽

2012 ◽

Vol 25 (20) ◽

pp. 7204-7215 ◽

Cited By ~ 20

Author(s):

Hui Wang ◽

Arun Kumar ◽

Wanqiu Wang ◽

Bhaskar Jha

Keyword(s):

Surface Temperature ◽

El Niño ◽

El Nino ◽

Surface Air Temperature ◽

Summer Precipitation ◽

Seasonal Prediction ◽

Atmospheric Models ◽

Temperature Anomalies ◽

Peak Phase ◽

Precipitation And Temperature

Abstract Evidence for spatially coherent, but different, U.S. summer precipitation and surface air temperature anomalies during the evolving phase and during the summers following the peak phase of the winter El Niño is presented. The spatial patterns during the decaying phase of El Niño are distinctive from patterns in the preceding summer when El Niño is in its evolving phase, that is, the traditional “simultaneous” composite patterns associated with El Niño. The analysis of a multimodel ensemble of global atmospheric models forced by observed sea surface temperature further confirms that the differences in the U.S. summer precipitation and surface temperature anomalies between the developing and decaying phases of El Niño are a result of the atmospheric response to tropical warm SST anomalies that are shifted eastward and are confined east of 120°W during the decaying phase of El Niño. Given the distinctive pattern, and relatively large amplitude of these anomalies during the decaying phase of El Niño, the results may have implications for the seasonal prediction of U.S. summer precipitation and temperature following winter El Niños.

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Improved Seasonal Prediction of Rainfall over East Africa for Application in Agriculture: Statistical Downscaling of CFSv2 and GFDL-FLOR

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-16-0365.1 ◽

2017 ◽

Vol 56 (12) ◽

pp. 3229-3243 ◽

Cited By ~ 6

Author(s):

O. Kipkogei ◽

A. M. Mwanthi ◽

J. B. Mwesigwa ◽

Z. K. K. Atheru ◽

M. A. Wanzala ◽

...

Keyword(s):

East Africa ◽

El Niño ◽

General Circulation ◽

Large Scale ◽

El Nino ◽

Circulation Model ◽

Seasonal Prediction ◽

Geophysical Fluid Dynamics Laboratory ◽

Climate Forecast ◽

Environmental Prediction

AbstractStatistically downscaled forecasts of October–December (OND) rainfall are evaluated over East Africa from two general circulation model (GCM) seasonal prediction systems. The method uses canonical correlation analysis to relate variability in predicted large-scale rainfall (characterizing, e.g., predicted ENSO and Indian Ocean dipole variability) to observed local variability over Kenya and Tanzania. Evaluation is performed for the period 1982–2011 and for the real-time forecast for OND 2015, a season when a strong El Niño was active. The seasonal forecast systems used are the National Centers for Environmental Prediction Climate Forecast System, version 2 (CFSv2), and the Geophysical Fluid Dynamics Laboratory Forecast-Oriented Low Ocean Resolution (GFDL-FLOR) version of CM2.5. The Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) rainfall dataset—a blend of in situ station observations and satellite estimates—was used at 5 km × 5 km resolution over Kenya and Tanzania as benchmark data for the downscaling. Results for the case-study forecast for OND 2015 show that downscaled output from both models adds realistic spatial detail relative to the coarser raw model output—albeit with some overestimation of rainfall that may have been derived from the downscaling procedure introducing a wet response to El Niño more typical of historical cases. Assessment of the downscaled forecasts over the 1982–2011 period shows positive long-term skill better than that documented in previous studies of unprocessed GCM forecasts for the region. Climate forecast downscaling is thus a key undertaking worldwide in the generation of more reliable products for sector specific application including agricultural planning and decision-making.

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A Next Generation (NextGen) Approach to Improve the Seasonal Prediction System in East Africa

10.1002/essoar.10504989.1 ◽

2020 ◽

Author(s):

Nachiketa Acharya ◽

Tufa Dinku ◽

Kyle Joseph Chen Hall

Keyword(s):

East Africa ◽

Seasonal Prediction ◽

Prediction System ◽

Next Generation

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The Late Quaternary history of climate and vegetation in East and southern Africa

Bothalia ◽

10.4102/abc.v14i3/4.1181 ◽

1983 ◽

Vol 14 (3/4) ◽

pp. 369-375 ◽

Cited By ~ 15

Author(s):

E. M. Van Zinderen Bakker Sr

Keyword(s):

East Africa ◽

Last Glacial Maximum ◽

Southern Africa ◽

Late Quaternary ◽

Summer Precipitation ◽

Vegetation Pattern ◽

Glacial Maximum ◽

Last Glacial ◽

The Last Glacial Maximum ◽

The Last Glacial

In the vast region of East and southern Africa the alternating glacial and interglacial periods of the Quaternarv were characterized by considerable changes in temperature and precipitation. During the last glacial maximum the influence of the ITCZ was limited, while the circulation systems were strengthened. The ocean surface waters were cooler and the Benguela Current was activated. In the montane areas of East Africa and also in southern Africa the temperature dropped by about 6°C. During this hypothermal period, rainfall on the east African plateau and mountains diminished. Summer precipitation could still penetrate the eastern half of southern Africa from the Indian Ocean, while the western half was arid to semi-arid. Cyclonic winter rain migrated further north beyond the latitude of the Orange River. The consequences of these climatic changes during the last glacial maximum were that the woodlands of East Africa opened up. On the plateau of South Africa austro-afroalpine vegetation dominated. The south coastal plain was very windy and cold to temperate, while the Namib and Kalahari were respectively hyper-arid and semi-humid. During hyperthermals the vegetation pattern resembled present-day conditions more closely.

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Improving Summer Precipitation Prediction in China Using Deep Learning

10.5194/egusphere-egu21-6338 ◽

2021 ◽

Author(s):

Weixin Jin ◽

Yong Luo

Keyword(s):

Deep Learning ◽

General Circulation ◽

Dynamic Models ◽

Summer Precipitation ◽

Seasonal Prediction ◽

Representation Learning ◽

Feature Representation ◽

Model Assessment ◽

State Variables ◽

Precipitation Prediction

Summer precipitation in China exhibits considerable spatial-temporal variation with direct social and economic impact. Yet seasonal prediction remains a long-standing challenge. The dynamical models even with a 1-month lead still shows limited forecast skill over China in summer. The present study focuses on applying deep learning to summer precipitation prediction in China. We train a convolutional neural network (CNN) on seasonal retrospective forecast from forecast centres in several European countries, and subsequently use transfer learning on reanalysis and observational data of 160 stations over China. The Pearson&#8217;s correlation coefficient (PCC) and the root mean square error (RMSE) are used to evaluate the performance of precipitation forecasts. The results demonstrate that deep learning approach produces skillful forecast better than those of current state-of-the-art dynamical forecast systems and traditional statistical methods in downscaling, with PCC increasing by 0.1&#8211;0.3, at 1&#8211;3 months leads. Moreover, experiments show that the data-driven model is capable to learn the complex relationship of input atmospheric state variables from reanalysis data and precipitation from station observations, with PCC of about 0.69. Image-Occlusion technique are also performed to determine variables and&#160; spatial features of the general circulation in the Northern Hemisphere which contribute maximally to the spatial distribution of summer precipitation in China through the automatic feature representation learning, and help evaluate the weakness of dynamic models, in order to gain a better understanding of the factors that limit the capability to seasonal prediction. It suggests that deep learning is a powerful tool suitable for both seasonal prediction and for dynamical model assessment.

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