Heavy snowfall during winter period (November to April) over the north-west Himalaya (NWH) generates many natural hazards. Time critical decisions primarily dependent on precipitation amount such as avalanche forecasting, flood forecasting, management of necessary services and supplies etc. demand real time weather forecasts, specially, quantitative precipitation forecasts (QPFs), at local scale during winter over the NWH. Two analog ensemble systems (AE1 system, AE2 system) for QPFs for varied forecast lead times are developed utilizing surface meteorological observations of 10 stations over the NWH. Performances of developed analog ensemble systems are evaluated and compared with performances of the climatological forecast models (CM1 model, CM2 model) for same forecast lead times for binary weather forecasts (precipitation day/no precipitation day) and QPFs at 10 stations over the NWH. The AE1 (AE2) system is found to perform better as compared to the CM1 (CM2) model for binary weather forecasts and QPFs at local scale over the NWH. Performance of the AE1 system for binary weather forecasts for shorter forecast lead times 0–15 h (0–15 h), 15–24 h, 24–39 h is found comparable to performance of the AE2 system for binary weather forecasts for longer forecast lead times 0–24 h, 24–48 h, 48–72 h. However, the Mean Absolute Errors (MAEs) and Root Mean Square Errors (RMSEs) for QPFs for shorter forecast lead times with the help of the AE1 system are found less as compared to the MAEs (RMSEs) for QPFs with the help of the AE2 system for longer forecast lead times. The AE1 system can provide real time QPFs based on recent surface meteorological observations for shorter lead times which can help in dynamic decision making for weather and avalanche forecasting over the NWH. However, QPFs with the help of the AE2 system can be useful for longer forecast lead times. Performances of the AE2 system and CM2 model are evaluated for binary weather forecasts and QPFs for a week period (7 days). The AE2 system is found to perform better as compared to the CM2 model for binary weather forecasts and QPFs for a week at local scale over the NWH. These results suggest that the AE2 system exhibits better consistency for QPFs as compared to the CM2 model for a week. The MAEs (RMSEs) for QPFs with the help of the AE1(AE2) system comparable to the MAEs (RMSEs) for QPFs with the help of other forecasting methods over the NWH (or elsewhere) suggest that the AE1(AE2) system exhibits good performance for real time QPFs at local scale over the NWH.

中文翻译：

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用于印度西北部喜马拉雅山 (NWH) 局部尺度不同预报提前期的实时定量降水预报 (QPF) 的模拟集合 (AE) 系统

喜马拉雅西北部 (NWH) 冬季（11 月至 4 月）的大雪会产生许多自然灾害。主要取决于降水量的时间关键决策，例如雪崩预报、洪水预报、必要服务和供应的管理等，需要实时天气预报，特别是在西北地区冬季局部尺度的定量降水预报 (QPF)。利用 NWH 上 10 个台站的地面气象观测，开发了用于不同预测提前期的 QPF 的两个模拟集合系统（AE1 系统、AE2 系统）。对开发的模拟集合系统的性能进行评估，并与气候预报模型（CM1 模型、CM2 模型）用于在 NWH 上的 10 个站点进行二元天气预报（降水日/无降水日）和 QPF 的相同预测提前期。与 CM1 (CM2) 模型相比，AE1 (AE2) 系统在 NWH 的局部尺度上的二元天气预报和 QPF 模型表现更好。AE1 系统用于更短预报提前期 0-15 小时（0-15 小时）、15-24 小时、24-39 小时的二进制天气预报的性能与用于更长预报的二进制天气预报的 AE2 系统的性能相当交货时间 0-24 小时、24-48 小时、48-72 小时。然而，与借助 AE2 的 QPF 的 MAE (RMSE) 相比，在 AE1 系统的帮助下缩短预测提前期的 QPF 的平均绝对误差 (MAE) 和均方根误差 (RMSE) 更少更长的预测提前期的系统。AE1 系统可以根据最近的地面气象观测提供实时 QPF，以缩短提前期，这有助于对 NWH 的天气和雪崩预报进行动态决策。但是，借助 AE2 系统的 QPF 可用于更长的预测提前期。AE2 系统和 CM2 模型的性能在一周（7 天）内针对二进制天气预报和 QPF 进行评估。发现 AE2 系统与 CM2 模型相比，在 NWH 的局部尺度上进行了一周的二元天气预报和 QPFs 的性能更好。这些结果表明，与 CM2 模型相比，AE2 系统在 QPF 方面表现出更好的一致性。