Improving prediction quality of sea surface temperature (SST) in Niño3.4 region using Bayesian Model Averaging

Prediction of Sea Surface Temperature (SST) in Niño3.4 region (170 W - 120 W; 5S - 5N) is important as a valuable indicator to identify El Niño Southern Oscillation (ENSO), i.e., El Niño, La Niña, and Neutral condition for coming months. More accurate prediction Niño3.4 SST can be used to determine the response of ENSO phenomenon to rainfall over Indonesia region. SST predictions are routinely released by meteorological institutions such as the European Center for Medium-Range Weather Forecasts (ECMWF). However, SST predictions from the direct output (RAW) of global models such as ECMWF seasonal forecast is suffering from bias that affects the poor quality of SST predictions. As a result, it also increases the potential errors in predicting the ENSO events. This study uses SST from the output Ensemble Prediction System (EPS) of ECMWF seasonal forecast, namely SEAS5. SEAS5 SST is downloaded from The Copernicus Climate Change Service (C3S) for period 1993-2020. One value representing SST over Niño3.4 region is calculated for each lead-time (LT), LT0-LT6. Bayesian Model Averaging (BMA) is selected as one of the post-processing methods to improve the prediction quality of SEAS5-RAW. The advantage of BMA over other post-processing methods is its ability to quantify the uncertainty in EPS, which is expressed as probability density function (PDF) predictive. It was found that the BMA calibration process reaches optimal performance using 160 months training window. The result show, prediction quality of Niño3.4 SST of BMA output is superior to SEAS5-RAW, especially for LT0, LT1, and LT2. In term deterministic prediction, BMA shows a lower Root Mean Square Error (RMSE), higher Proportion of Correct (PC). In term probabilistic prediction, the error rate of BMA, which is showed by the Brier Score is lower than RAW. Moreover, BMA shows a good ability to discriminating ENSO events which indicates by AUC ROC close to a perfect score.

Statistical Analysis of Rainfall in Haryana

The following research focuses on rainfall of Haryana for past 50 years i.e. from 1968 to 2017. Parameters like Kurtosis, Variance, Goodness of Fit, Mann-Kendall’s Test were performed along with total annual forecast as well as seasonal forecast was predicted. Seasonal rend was also studied so as to study in detail about the past, present, and future of rainfall in Chandigarh. This study was performed with the help of MS-Excel and ExcelStat. A rising trend was found along with total rainfall in of past 5 decades was 513336.2 mm and The maximum and minimum rainfall during this period was found to be 15126.62 mm in 1976 and 5312.51 in 1987 respectively.

Statistical analysis of seasonal rainfall data in Chandigarh: A case study

The following research focuses on Chandigarh’s annual rainfall of past 50 years i.e. from 1968 to 2017. Parameters like Kurtosis, Variance, Goodness of Fit, Mann-Kendall’s Test were performed along with total annual forecast as well as seasonal forecast was predicted. Seasonal rend was also studied so as to study in detail about the past, present, and future of rainfall in Chandigarh. This study was performed with the help of MS-Excel and ExcelStat. A rising trend was found in Chandigarh for total as well as seasonal rainfall with a maximum rainfall of 1510.9 mm in the year of 1996 and a minimum of 371.1 mm in year 1987, other than this Sen.’s slope was 6.431 whereas skewness was found to be 0.6018.

Understanding Rainfall Prediction Skill Over the Sahel in NMME Seasonal Forecast

Sahelian rainfall presents large interannual variability which is partly controlled by the sea surface temperature anomalies (SSTa) over the eastern Mediterranean, equatorial Pacific and Atlantic oceans, making seasonal prediction of rainfall changes in Sahel potentially possible. However, it is not clear whether seasonal forecast models present skill to predict the Sahelian rainfall anomalies. Here, we consider the set of models from the North American Multi-model ensemble (NMME) and analyze their skill in predicting the Sahelian precipitation and address the sources of this skill.Results show that though the skill in predicting the Sahelian rainfall is generally low, it and can be mostly explained by a combination of how well models predict the SSTa in the Mediterranean and in the equatorial Pacific regions, and how well they simulate the teleconnections of these SSTa with Sahelian rainfall. Our results suggest that Sahelian rainfall skill is improved for those models in which the Pacific SST - Sahel rainfall teleconnection is correctly simulated. On the other hand, models present a good ability to reproduce the sign of the Mediterranean SSTa – Sahel teleconnection, albeit with underestimated amplitude due to an underestimation of the variance of the SSTa over this oceanic region. However, they fail to correctly predict the SSTa over this basin, which is the main reason for the poor Sahel rainfall skill in models. Therefore, results suggest models need to improve their ability to reproduce the variability of the SSTa over the Mediterranean as well as the teleconnections of Sahelian rainfall with Pacific and Mediterranean SSTa.

Analysis of a Short-Term and a Seasonal Precipitation Forecast Over Kenya

Kenya is highly dependent on precipitation for both food and water security. Farmers and pastoralists rely on rain to provide water for crops and vegetation to feed herds. As such, precipitation forecasts can be useful tools to inform decision makers and potentially allow the preparation for such events as drought. This study assessed the predictability of a seasonal forecast (CFSv2) and a short-term precipitation forecast (CHIRPS-GEFS) over Kenya. The short-term forecast was assessed on its ability to predict the onset date of the rainy season, and the skill of the seasonal forecast in predicting abnormal precipitation patterns. CHIRPS-GEFS provided a useful starting point to estimate the onset date, but during the long rains in the southwest, where agriculture is concentrated, differences between the predicted and actual onset dates were large (over 20 days). Assessments for CFSv2 generally displayed lower forecast skill over highlands and coastal regions at a seasonal scale. The CFSv2 forecast skill varied widely over individual months and lead times, but over whole rainy seasons, CFSv2 was more skillful than a random forecast at all lead times in the major agricultural areas of Kenya. This research fills a critical research and application gap in understanding the forecast precipitation skill for onset and sub-seasonal prediction.