The National Centers for Environmental Prediction‐National Center for Atmospheric Research (NCEP‐NCAR) monthly mean reanalysis dataset has been used to analyze spatial variations of summertime subtropical anticyclones over the Asia–Africa region. The geopotential height and zonal wind components of 1000, 500, 200, and 100 hPa in a 30‐year period (1971–2000) have been used to determine the spatial and temporal variations of the anticyclone centres, their monthly frequency and latitudinal axis variations during April–October.The results revealed that there is a clear difference in the location of the summer anticyclone centres in lower, middle and upper levels of the troposphere. In the lower levels, the Azores subtropical anticyclone is located at the east of North Atlantic. In the middle levels, the frequencies of anticyclone centre are concentrated over the northwest of Africa, Arabian Peninsula and Iranian Plateau. In the upper troposphere, the geographical location of the anticyclone centres and their frequencies in the summer season exhibit a scattered pattern from south of China up to western Iran at 200 hPa, and a bimodal pattern over the Tibetan and the Iranian Plateaus at 100 hPa. In fact, in the entire study domain, the Iranian Plateau is a preferable location of the middle and upper troposphere anticyclones.The highest observed latitude of the subtropical anticyclone at 100, 200 and 500 hPa levels have been seen over north of Tibetan plateau, a large area from east to west of Asia and Iran during August, July–August and July, respectively. The maximum monthly variation in the latitude of the ridgeline is seen at 500, 200, and 100 hPa from June to July which goes even up to 10 degrees at some longitudes. Copyright © 2009 Royal Meteorological Society

The numerical weather forecast model Weather Research and Forecasting (WRF) has a range of applications because it offers multiple physical options, enabling the users to optimizing WRF for specific scales, geographical locations and applications. Summer rainfall cannot be predicted well in North West of Iran (NWI). Most of them are convective. Sometimes rainfall is heavy, so that it causes flash flood. In this research, some configurations of WRF were tested with four summer rainfall events in NWI to find the best configuration. Five cumulus, four planetary boundary layers (PBL) and two microphysical schemes were combined. Twenty-six different configurations (models) were implemented at two resolutions of 5 and 15 km for duration of 48 h. Four events, with over 20 mm convective daily rainfall total, were selected at NWI during summer season between 2010 and 2015. These events were tested by developing 26 unique models. Results were verified using several methods. The aim was to find the best results during the first 24 h. Although no single configuration can be introduced for all times, thresholds, and atmospheric system to provide reliable and accurate forecast, the best configuration for WRF can be identified. Kain-Fritsch (new Eta), Betts-Miller-Janjic, Modified Kain-Fritsch, Multi-scale Kain-Fritsch and newer Tiedtke cumulus schemes and Mellor-Yamada-Janjic, Shin-Hong ‘scale-aware’, Medium Range Forecast (MRF) and Yonsei University (YSU) Planetary Boundary Layer schemes and Kessler, WRF Single Moment 3 class simple ice (WSM3) microphysics schemes were selected. The result show that Cumulus schemes are the most sensitive and Microphysics schemes are the less sensitive. The comparison of 15 km and 5 km resolution simulations do not show obvious advantages in downscaling the results. Configuration with newer Tiedtke cumulus, Mellor-Yamada-Janjic PBL, WSM3 and Kessler microphysics schemes give the best results for the 5 and 15 km resolutions. The output image of models and statistical methods verification indexes show that WRF could not accurately simulate convective rainfall in the NWI in summer.

Low visibility conditions due to fog affect air traffic and, in some cases, are the leading cause of aviation accidents. Accurate forecasting of fog can lead to a significant reduction of human and financial losses. Numerical weather prediction (NWP) models have limitations in simulating fog accurately for operational purposes. However, recent studies have confirmed that ensemble‐based forecasts are effective in fog forecasting. In this study, a multi‐physics ensemble prediction system (EPS) was used to simulate the occurrence of several radiation, cloud base lowering (CBL), and advection fog events that occurred at Arak, Ardebil, Hamedan, Orumiyeh, Rasht and Shahrekord airports located across Iran, during January and December 2015. The multi‐physics EPS including a reference deterministic forecast consists of 16 different configurations of the weather research and forecasting (WRF) model and was run from 12 UTC 12 January to 12 UTC 16 January 2015 and from 00 UTC 27 December to 12 UTC 31 December 2015 to simulate fog occurrences at six airports. SW99 and G2009 visibility algorithms were applied to the EPS outputs to predict the fog. The advantages of probabilistic fog forecasting in this study were shown by comparing the reference forecast and the ensemble‐based forecasts of fog events. The results showed that by considering a probability threshold of 37.5%, 50% and 62.5% for the ensemble forecasts, the EPS outperformed the deterministic fog forecasts obtained from a deterministic reference forecast. Such that, with 37.5% and 50% probability thresholds, the equitable threat score (ETS) was much higher than the ETS for the reference deterministic fog forecast. Also, the EPS with 37.5% and 50% probability thresholds could correctly predict 3 and 4 (out of 10) more fog events compared with the reference deterministic forecast.

All numerical weather prediction (NWP) models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate (IDSW-CLR), Kriging with constant lapse rate (Kriging-CLR), gradient inverse distance squared with linear lapse rate (GIDS-LR), and gradient inverse distance squared with lapse rate determined by classification and regression tree (GIDS-CART), are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly, but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error (RMSE) and mean absolute error (MAE) were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency.

In this study a non‐hydrostatic version of the PSU/NCAR mesoscale model is used to simulate an active western disturbance (WD) that affected the Indian region in January 1997. The role of the planetary boundary layer (PBL) and convection parameterisation schemes in the development of the WD is investigated. Analysis and predictions for some fields, including sea level pressure, geopotential height, temperature, horizontal wind and precipitation are examined. Some statistical scores are also calculated and compared. It is found that the performance of the Hong‐Pan (as implemented in the NCAR MRF model) and Betts‐Miller (or Grell) schemes as PBL and convection parameterisation schemes respectively are best compared to the other schemes used in this study. Copyright © 2002 Royal Meteorological Society.