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  • Author or Editor: T. N. Krishnamurti x
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Akiyo Yatagai
,
T. N. Krishnamurti
,
Vinay Kumar
,
A. K. Mishra
, and
Anu Simon

Abstract

A multimodel superensemble developed by the Florida State University combines multiple model forecasts based on their past performance (training phase) to make a consensus forecast. Because observed precipitation reflects local characteristics such as orography, quantitative high-resolution precipitation products are useful for downscaling coarse model outputs. The Asian Precipitation–Highly-Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) and Tropical Rainfall Measuring Mission (TRMM) 3B43 products are used for downscaling and as training data in the superensemble training phase. Seven years (1998–2004) of monthly precipitation (June–August) over the Asian monsoon region (0°–50°N, 60°–150°E) and results of four coupled climate models were used. TRMM 3B43 was adjusted by APHRODITE (m-TRMM). For seasonal climate forecasts, a synthetic superensemble technique was used. A cross-validation technique was adopted, in which the year to be forecast was excluded from the calculations for obtaining the regression coefficients. The principal results are as follows: 1) Seasonal forecasts of Asian monsoon precipitation were considerably improved by use of APHRODITE rain gauge–based data or the m-TRMM product. These forecasts are much superior to those from the best model of the suite and ensemble mean. 2) Use of a statistical downscaling and synthetic superensemble method for multimodel forecasts of seasonal climate significantly improved precipitation prediction at higher resolution. This is confirmed by cross-evaluation of superensemble with using other observation data than the data used in the training phase. 3) Availability of a dense rain gauge network–based analysis was essential for the success of this work.

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T. N. Krishnamurti
,
C. M. Kishtawal
,
D. W. Shin
, and
C. Eric Williford

Abstract

This paper utilizes forecasts from a multianalysis system to construct a superensemble of precipitation forecasts. This method partitions the computations into two time lines. The first of those is a control (or a training) period and the second is a forecast period. The multianalysis is derived from a physical initialization–based data assimilation of “observed rainfall rates.” The different members of the reanalysis are produced by using different rain-rate algorithms for physical initialization. The basic rain-rate datasets are derived from satellites’ microwave radiometers, including those from the Tropical Rainfall Measuring Mission (TRMM) satellites and the Special Sensor Microwave Imager (SSM/I) data from three current U.S. Air Force Defense Meteorological Satellite Program (DMSP) satellites. During the training period, 155 experiments were conducted to find the relationship between forecasts from the multianalysis dataset and the best “observed” estimates of daily rainfall totals. This relationship is based on multiple regression and defined by statistical weights (which vary in space.) The forecast phase utilizes the multianalysis forecasts and the statistics from the training period to produce superensemble forecasts of daily rainfall totals. The results for day 1, day 2, and day 3 forecasts are compared to various conventional forecasts with a global model. The superensemble day 3 forecasts of precipitation clearly have the highest skill in such comparisons.

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T. N. Krishnamurti
,
David Bachiochi
,
Timothy LaRow
,
Bhaskar Jha
,
Mukul Tewari
,
D. R. Chakraborty
,
Ricardo Correa-Torres
, and
Darlene Oosterhof

Abstract

This study is based on a global coupled atmosphere–ocean model climate prediction that was designed to include 14 layers over the atmosphere and 17 layers within the ocean. In this model an 11-yr data assimilation includes physical initialization of the daily rainfall estimates. No flux corrections are included in the seasonal and annual forecasts of this coupled model. It is first shown that intraseasonal oscillation on the Madden–Julian timescale was an important feature during the onset of the El Niño of 1997. It is shown that this feature is retained in the model’s data assimilation and in the forecasts. The forecasts commence on 1 April 1997. The model forecasts showed an El Niño warming of the equatorial Pacific Ocean waters commencing with the excitation of a Kelvin wave. The Niño-3.4 region acquired above-normal sea surface temperature anomalies (SSTAs) by 15 May. The warm SSTs reached a peak by around January 1998. The El Niño made its demise by June 1998. The life cycle of the entire SSTA shows remarkable agreement to the observed anomalies over the Pacific Ocean. The subsurface temperature anomalies exhibit eastward propagating subsurface warm and cold water that are in phase with the El Niño and the La Niña features at the surface. Phenomenologically, this study is quite successful in showing the following.

  • Velocity potential anomalies at the 200-hPa level are good indicators for long-lasting dry spells. In particular the authors have remarkable success in predicting the long-lasting dry spell over Florida (which resulted in major fires over Florida during June 1998, some 14 months into the forecast) and over Indonesia (which resulted in major fires over Indonesia during September and October 1997). This was by far the most promising result of the coupled modeling study. This study also enumerates several areas of the climate of 1997–98 that were not reasonably simulated at the present resolution of the coupled model. The model does not exhibit very high skill in prediction of precipitation anomalies over the Asian–Australian monsoon world, which is most likely due to the resolution and organization of convection issues.

  • A realistic picture is shown of the North American monsoon system (the Mexico–Arizona monsoon) with wet conditions along 110°W, dry conditions along 95°W, and wet conditions along 80°W during the summers of 1997 and 1998. Furthermore, the model successfully shows a stronger North American monsoon system during the post–El Niño year 1998 compared to the El Niño year 1997. This is in accordance with the climatological and observational findings.

  • California rainfall during January and February 1998, arising from the eastward passage of disturbances from the Pacific Ocean, was successfully simulated, although the rainfall amounts at the model resolution were roughly one-third of the observed peak estimates.

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T. N. Krishnamurti
,
C. M. Kishtawal
,
Zhan Zhang
,
Timothy LaRow
,
David Bachiochi
,
Eric Williford
,
Sulochana Gadgil
, and
Sajani Surendran

Abstract

In this paper the performance of a multimodel ensemble forecast analysis that shows superior forecast skills is illustrated and compared to all individual models used. The model comparisons include global weather, hurricane track and intensity forecasts, and seasonal climate simulations. The performance improvements are completely attributed to the collective information of all models used in the statistical algorithm.

The proposed concept is first illustrated for a low-order spectral model from which the multimodels and a “nature run” were constructed. Two hundred time units are divided into a training period (70 time units) and a forecast period (130 time units). The multimodel forecasts and the observed fields (the nature run) during the training period are subjected to a simple linear multiple regression to derive the statistical weights for the member models. The multimodel forecasts, generated for the next 130 forecast units, outperform all the individual models. This procedure was deployed for the multimodel forecasts of global weather, multiseasonal climate simulations, and hurricane track and intensity forecasts. For each type an improvement of the multimodel analysis is demonstrated and compared to the performance of the individual models. Seasonal and multiseasonal simulations demonstrate a major success of this approach for the atmospheric general circulation models where the sea surface temperatures and the sea ice are prescribed. In many instances, a major improvement in skill over the best models is noted.

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