Use of APHRODITE Rain Gauge–Based Precipitation and TRMM 3B43 Products for Improving Asian Monsoon Seasonal Precipitation Forecasts by the Superensemble Method

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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Validation of TRMM 3B42V7 Rainfall Product under Complex Topographic and Climatic Conditions over Hexi Region in the Northwest Arid Region of China

Water ◽

10.3390/w10081006 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1006 ◽

Cited By ~ 4

Author(s):

Xiuna Wang ◽

Yongjian Ding ◽

Chuancheng Zhao ◽

Jian Wang

Keyword(s):

Bias Correction ◽

Arid Region ◽

Rain Gauge ◽

Climatic Conditions ◽

Monthly Precipitation ◽

Observation Data ◽

Precipitation Data ◽

Rain Gauges ◽

Hexi Region ◽

Precipitation Events

Continuous and accurate spatiotemporal precipitation data plays an important role in regional climate and hydrology research, particularly in the arid inland regions where rain gauges are sparse and unevenly distributed. The main objective of this study is to evaluate and bias-correct the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 rainfall product under complex topographic and climatic conditions over the Hexi region in the northwest arid region of China with the reference of rain gauge observation data during 2009–2015. A series of statistical indicators were adopted to quantitatively evaluate the error of 3B42V7 and its ability in detecting precipitation events. Overall, the 3B42V7 overestimates the precipitation with Bias of 11.16%, and its performance generally becomes better with the increasing of time scale. The agreements between the rain gauge data and 3B42V7 are very low in cold season, and moderate in warm season. The 3B42V7 shows better correlation with rain gauges located in the southern mountainous and central oasis areas than in the northern extreme arid regions, and is more likely to underestimate the precipitation in high-altitude mountainous areas and overestimate the precipitation in low-elevation regions. The distribution of the error on the daily scale is more related to the elevation and rainfall than in monthly and annual scale. The 3B42V7 significantly overestimates the precipitation events, and the overestimation mainly focuses on tiny amounts of rainfall (0–1 mm/d), which is also the range of false alarm concentration. Bias correction for 3B42V7 was carried out based on the deviation of the average monthly precipitation data during 2009–2015. The bias-corrected 3B42V7 was significantly improved compared with the original product. Results suggest that regional assessment and bias correction of 3B42V7 rainfall product are of vital importance and will provide substantive reference for regional hydrological studies.

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On the Interpretation and Utility of Skill Information for Seasonal Climate Predictions

Monthly Weather Review ◽

10.1175/mwr3385.1 ◽

2007 ◽

Vol 135 (5) ◽

pp. 1974-1984 ◽

Cited By ~ 12

Author(s):

Arun Kumar

Keyword(s):

Time Series ◽

General Circulation ◽

Circulation Model ◽

Seasonal Prediction ◽

Prediction Skill ◽

Steady Increase ◽

Seasonal Forecasts ◽

Past Performance ◽

Seasonal Climate ◽

The Past

Abstract In recent years, there has been a steady increase in the emphasis on routine seasonal climate predictions and their potential for enhancing societal benefits and mitigating losses related to climate extremes. It is also suggested by the users, as well as by the producers of climate predictions, that for informed decision making, real-time seasonal climate predictions should be accompanied by a corresponding level of skill estimated from a sequence of the past history of forecasts. In this paper it is discussed whether conveying skill information to the user community can indeed deliver the promised benefits or whether issues inherent in the estimates of seasonal prediction skill may still lead to potential misinterpretation of the information content associated with seasonal predictions. Based on the analysis of atmospheric general circulation model simulations, certain well-known, but often underappreciated, issues inherent in the estimates of seasonal prediction skill from the past performance of seasonal forecasts are highlighted. These include the following: 1) the stability of estimated skill depends on the length of the time series over which seasonal forecasts are verified, leading to scenarios where error bars on the estimated skill could be of the same magnitude as the skill itself; 2) a single estimate of skill obtained from the verification over a given forecast time series, because of variation in the signal-to-noise ratio from one year to another, is generally not representative of seasonal prediction skill conditional to sea surface temperature anomalies on a case-by-case basis. These issues raise questions on the interpretation, presentation, and utilization of skill information for seasonal prediction efforts and present opportunities for increased dialogue and the exploration of ways for their optimal utilization by decision makers.

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REVIEW OF SEASONAL CLIMATE FORECASTING FOR AGRICULTURE IN SUB-SAHARAN AFRICA

Experimental Agriculture ◽

10.1017/s0014479710000876 ◽

2011 ◽

Vol 47 (2) ◽

pp. 205-240 ◽

Cited By ~ 148

Author(s):

JAMES W. HANSEN ◽

SIMON J. MASON ◽

LIQIANG SUN ◽

ARAME TALL

Keyword(s):

Smallholder Farmers ◽

Seasonal Forecast ◽

Sub Saharan Africa ◽

Seasonal Forecasting ◽

Seasonal Forecasts ◽

Climate Services ◽

Seasonal Climate ◽

Climate Forecasting ◽

Probabilistic Forecasts ◽

Sub Saharan

SUMMARYWe review the use and value of seasonal climate forecasting for agriculture in sub-Saharan Africa (SSA), with a view to understanding and exploiting opportunities to realize more of its potential benefits. Interaction between the atmosphere and underlying oceans provides the basis for probabilistic forecasts of climate conditions at a seasonal lead-time, including during cropping seasons in parts of SSA. Regional climate outlook forums (RCOF) and national meteorological services (NMS) have been at the forefront of efforts to provide forecast information for agriculture. A survey showed that African NMS often go well beyond the RCOF process to improve seasonal forecast information and disseminate it to the agricultural sector. Evidence from a combination of understanding of how climatic uncertainty impacts agriculture, model-based ex-ante analyses, subjective expressions of demand or value, and the few well-documented evaluations of actual use and resulting benefit suggests that seasonal forecasts may have considerable potential to improve agricultural management and rural livelihoods. However, constraints related to legitimacy, salience, access, understanding, capacity to respond and data scarcity have so far limited the widespread use and benefit from seasonal prediction among smallholder farmers. Those constraints that reflect inadequate information products, policies or institutional process can potentially be overcome. Additional opportunities to benefit rural communities come from expanding the use of seasonal forecast information for coordinating input and credit supply, food crisis management, trade and agricultural insurance. The surge of activity surrounding seasonal forecasting in SSA following the 1997/98 El Niño has waned in recent years, but emerging initiatives, such as the Global Framework for Climate Services and ClimDev-Africa, are poised to reinvigorate support for seasonal forecast information services for agriculture. We conclude with a discussion of institutional and policy changes that we believe will greatly enhance the benefits of seasonal forecasting to agriculture in SSA.

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Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index

Hydrology and Earth System Sciences ◽

10.5194/hess-17-2359-2013 ◽

2013 ◽

Vol 17 (6) ◽

pp. 2359-2373 ◽

Cited By ~ 62

Author(s):

E. Dutra ◽

F. Di Giuseppe ◽

F. Wetterhall ◽

F. Pappenberger

Keyword(s):

Real Time ◽

Lead Time ◽

Standardized Precipitation Index ◽

Precipitation Index ◽

Seasonal Forecasting ◽

Monthly Precipitation ◽

Seasonal Forecasts ◽

Blue Nile ◽

Forecasting System ◽

The Standardized Precipitation Index

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.

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Seasonal drought prediction for semiarid northeastern Brazil: verification of six hydro-meteorological forecast products

Hydrology and Earth System Sciences ◽

10.5194/hess-22-5041-2018 ◽

2018 ◽

Vol 22 (9) ◽

pp. 5041-5056 ◽

Cited By ~ 4

Author(s):

José Miguel Delgado ◽

Sebastian Voss ◽

Gerd Bürger ◽

Klaus Vormoor ◽

Aline Murawski ◽

...

Keyword(s):

Rainy Season ◽

Meteorological Drought ◽

Skill Score ◽

Hydrological Drought ◽

Northeastern Brazil ◽

Drought Indices ◽

Monthly Precipitation ◽

Seasonal Forecasts ◽

Seasonal Drought ◽

Drought Prediction

Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.

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Development of a daily gridded precipitation data set for the Middle East

Advances in Geosciences ◽

10.5194/adgeo-12-165-2008 ◽

2008 ◽

Vol 12 ◽

pp. 165-170 ◽

Cited By ~ 51

Author(s):

A. Yatagai ◽

P. Xie ◽

P. Alpert

Keyword(s):

Middle East ◽

Climate Models ◽

Daily Precipitation ◽

Rain Gauge ◽

Monthly Precipitation ◽

Data Set ◽

Key Points ◽

Fertile Crescent ◽

Precipitation Analysis ◽

The Impact

Abstract. We show an algorithm to construct a rain-gauge-based analysis of daily precipitation for the Middle East. One of the key points of our algorithm is to construct an accurate distribution of climatology. One possible advantage of this product is to validate high-resolution climate models and/or to diagnose the impact of climate changes on local hydrological resources. Many users are familiar with a monthly precipitation dataset (New et al., 1999) and a satellite-based daily precipitation dataset (Huffman et al., 2001), yet our data set, unlike theirs, clearly shows the effect of orography on daily precipitation and other extreme events, especially over the Fertile Crescent region. Currently the Middle-East precipitation analysis product is consisting of a 25-year data set for 1979–2003 based on more than 1300 stations.

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Forecasting hydrologic parameters using linear and nonlinear stochastic models

Journal of Water and Climate Change ◽

10.2166/wcc.2019.249 ◽

2019 ◽

Vol 11 (4) ◽

pp. 1284-1301

Author(s):

Hamed Nozari ◽

Fateme Tavakoli

Keyword(s):

Support Vector Machine ◽

Moving Average ◽

Sustainable Use ◽

Rain Gauge ◽

Support Vector ◽

Autoregressive Moving Average ◽

Monthly Precipitation ◽

Precipitation Prediction ◽

Armax Model ◽

Monthly Discharge

Abstract One of the most important bases in the management of catchments and sustainable use of water resources is the prediction of hydrological parameters. In this study, support vector machine (SVM), support vector machine combined with wavelet transform (W-SVM), autoregressive moving average with exogenous variable (ARMAX) model, and autoregressive integrated moving average (ARIMA) models were used to predict monthly values of precipitation, discharge, and evaporation. For this purpose, the monthly time series of rain-gauge, hydrometric, and evaporation-gauge stations located in the catchment area of Hamedan during a 25-year period (1991–2015) were used. Out of this statistical period, 17 years (1991–2007), 4 years (2008–2011), and 4 years (2012–2015) were used for training, calibration, and validation of the models, respectively. The results showed that the ARIMA, SVM, ARMAX, and W-SVM ranked from first to fourth in the monthly precipitation prediction and SVM, ARIMA, ARMAX, and W-SVM were ranked from first to fourth in the monthly discharge and monthly evaporation prediction. It can be said that the SVM has fewer adjustable parameters than other models. Thus, the model is able to predict hydrological changes with greater ease and in less time, because of which it is preferred to other methods.

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Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

Scientific Reports ◽

10.1038/s41598-019-53797-9 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 20

Author(s):

Teja Kattenborn ◽

Jana Eichel ◽

Fabian Ewald Fassnacht

Keyword(s):

Neural Networks ◽

High Resolution ◽

Convolutional Neural Networks ◽

Low Cost ◽

Training Data ◽

Observation Data ◽

Visual Interpretation ◽

Fine Grained ◽

Wide Range ◽

Vegetation Species

AbstractRecent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies in high spatial detail. Efficient methods are needed to fully harness this unpreceded source of information for vegetation mapping. Deep learning algorithms such as Convolutional Neural Networks (CNN) are currently paving new avenues in the field of image analysis and computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) in combination with training data directly derived from visual interpretation of UAV-based high-resolution RGB imagery for fine-grained mapping of vegetation species and communities. We demonstrate that this approach indeed accurately segments and maps vegetation species and communities (at least 84% accuracy). The fact that we only used RGB imagery suggests that plant identification at very high spatial resolutions is facilitated through spatial patterns rather than spectral information. Accordingly, the presented approach is compatible with low-cost UAV systems that are easy to operate and thus applicable to a wide range of users.

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Improved Seasonal Precipitation Forecasts for the Asian Monsoon Using 16 Atmosphere–Ocean Coupled Models. Part II: Anomaly

Journal of Climate ◽

10.1175/2011jcli4126.1 ◽

2012 ◽

Vol 25 (1) ◽

pp. 65-88 ◽

Cited By ~ 9

Author(s):

T. N. Krishnamurti ◽

Vinay Kumar

Keyword(s):

Asian Monsoon ◽

Monsoon Rainfall ◽

Systematic Errors ◽

Rainfall Anomaly ◽

Seasonal Rainfall ◽

Seasonal Precipitation ◽

Training Phase ◽

Post Processing ◽

Coupled Models ◽

Rms Errors

Abstract This is the second part of a paper on the improved seasonal precipitation forecasts for the Asian monsoon using 16 atmosphere–ocean coupled models. This study utilizes a large suite of coupled atmosphere–ocean models; this second part largely addresses the skill of rainfall anomaly forecasts. These include both deterministic and probabilistic skill measures such as the RMS errors, anomaly correlations, equitable threat scores, and the Brier skill score. It was possible to improve the skills of rainfall climatology from the use of a downscaled multimodel superensemble to very high levels, and it is of interest to ask how far this methodology would go toward improving the skills of seasonal rainfall anomaly forecasts. It is possible to go through a sequence of multimodel post processing to improve upon these skills by using a dense rain gauge network over Asia, downscaling forecasts for each member model, and constructing a multimodel superensemble that benefits from the persistence of errors of the member models. This paper addresses the spinup issues of the downscaling and the superensemble results where the number of years of model data needed for training phase, for the downscaling, and for the construction of the superensemble, is addressed. In the context of cross validation, the training phase includes 14 seasons of monsoon data. The forecast phase is only one season; it is this season that was not included in the training phase each time. The relationship between data length and the number of models needed for enhanced skills is another issue that is addressed. Seasonal climate forecasts over the larger monsoon Asia domain and over the regional belts are evaluated. The superensemble forecasts invariably have the highest skill compared to the member models globally and regionally. This is largely due to the presence of large systematic errors in models that carry low seasonal prediction skills. Such models carry persistent signatures of systematic errors, and their errors are recognized by the multimodel superensemble. The probabilistic skills show that the superensemble-based forecasts carry a much higher reliability score compared to the member models. This implies that the superensemble-based forecasts are the most reliable among all the member models. It is possible to examine the performance of models and of the superensemble during periods of heavy monsoon rainfall versus those for deficient monsoon rainfall seasons. One of the conclusions of this study is that given the uncertainties in current modeling for seasonal rainfall forecasts, post processing of multimodel forecasts, using the superensemble methodology, seems to provide the most promising results for the rainfall anomaly forecasts. These results are confirmed by an additional skill metric where the RMS errors and the correlations of forecast skills are evaluated using a normalized precipitation anomaly for the forecasts and the observed estimates.

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