This study investigates the long‐term variability of surface air temperature (SAT) over the Arabian Peninsula (AP), using data from the Climate Research Unit (TS 3.22) for the 1960–2010 period. The long‐term climatology suggests that the warmest AP mean temperatures occur during summer, with the highest temperatures over the northern AP (NAP), due to the monsoon–desert mechanism. During winter, the NAP exhibits low SATs under the influence of western disturbances originating from the Mediterranean. The southwestern AP exhibits the lowest temperatures because of its proximity to the Arabian Sea cold waters, and also because of the orographic effects. The inter‐annual variability of the SAT is stronger during winters. A linear trend analysis reveals a significant increase in the SAT anomaly (0.10 °C/decade) across the AP, consistently with the global temperature anomalies. Besides the local convective heating, summer SAT variability is associated with the weakening of the Asian jet stream and a Rossby wave train from the Indian Ocean. This variability is also influenced by the anomalous low pressure over the North Atlantic and the Sahara, a high‐pressure system over Siberia and the northwest Pacific. Both in spring and autumn, sea surface temperature (SST) variations over the Indo‐western Pacific are highly influenced the AP SATs, whereas winter SATs are modulated by the subtropical jet stream and the Middle East jet stream. In all seasons, the AP SAT is strongly influenced by the SST variations over the tropical oceans. The temperature variability is closely associated with the El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO) and Arctic Oscillation (AO). The warm phase of ENSO (i.e., El Niño) is one possible reason behind the inter‐annual increase in SAT over the southern AP. The negative phases of NAO and AO also play a role in increasing AP SAT.

Considerable uncertainties are associated with precipitation characteristics over the western Himalayan region (WHR). These are due to typically small-scale but high-intensity storms caused by the complex topography that are under-resolved by a sparse gauge network. Additionally, both satellite and gauge precipitation measurements remain subject to systematic errors, typically resulting in underestimation over mountainous terrains. Reanalysis datasets provide prospective alternative but are limited by their resolution, which has so far been too coarse to properly resolve orographic precipitation. In this study, we evaluate and cross compare Indian Monsoon Data Assimilation and Analysis (IMDAA), the first high-resolution (12 km) regional reanalysis over India, with various precipitation products during winter season over WHR. We demonstrate IMDAA’s efficiency in representing winter precipitation characteristics at seasonal, diurnal, interannual scales, as well as heavy precipitation associated with western disturbances (WDs). IMDAA shows closer agreement to other reanalyses than to gauge-based and satellite products in error and bias analysis. Although depicting higher magnitudes, its fine resolution allows a much closer insight into localized spatial patterns and the diurnal cycle, a key advantage over other datasets. Mean winter precipitation over WHR shows a significant decreasing trend in IMDAA, despite no significant trend in the frequency of WDs tracked in either IMDAA or ERA5. The study also exhibits the potential use of IMDAA for characterizing winter atmospheric dynamics, both for climatological studies and during WD activity such as localized valley winds. Overall, these findings highlight the potential utility for IMDAA in conducting monitoring and climate change impact assessment studies over the fragile western Himalayan ecosystem.

The sensitivity of different cumulus physical parameterization schemes for simulating extreme winter precipitation events over the Arabian Peninsula (AP) is investigated using a high‐resolution weather research and forecasting (WRF) model. For winters in 2001–2016, the following three parameterization schemes are examined: (i) Kain–Fritsch (KF), (ii) Betts–Miller–Janjić (BMJ), and (iii) Grell–Freitas (GF). The simulation results suggest that the AP extreme winter rainfall events are best simulated using the KF, followed by the BMJ, in terms of spatial distribution and intensity. The spatial pattern correlation coefficient between the model‐simulated and observed rainfall is highest with KF (0.94), followed by BMJ (0.91) and GF (0.76). These results are attributed to a better representation of the moisture transport associated with upper‐tropospheric cyclonic circulation and potential vorticity intrusions. By contrast, the GF scheme fails to simulate moisture convergence and updrafts, leading to an unrealistic representation of cloud hydrometeors and an improper organization of convection and associated extreme rainfall intensities. Meanwhile, the KF and BMJ also successfully simulate the dynamics and thermodynamics of extreme rainfall events that are usually driven by synoptic forcing. The study results suggest that the choice of cumulus parameterization schemes in the WRF model is critical for reliable simulation of extreme rainfall in the hyperarid AP region.

This study investigates the dominant modes of surface air temperature (SAT) variability and associated circulation changes over the Arabian Peninsula (AP) during summer for the period 1979–2016 based on an empirical orthogonal function (EOF) analysis. The analysis results reveal that the first leading EOF mode is related to the weakening of the subtropical westerly jet stream, which may impact the AP temperature variability through the mid‐latitude Rossby wave trains (successive troughs and ridges). This can be explained by the high correlation of the AP summer temperatures with the quasi‐stationary mid‐latitude/extratropical Eurasian Rossby wave train type patterns, which influences the air temperature variability by modulating the Asian Jets. Furthermore, the high AP SAT variability is also closely associated with strong middle to lower tropospheric descent (subsidence) anomalies, which cause warm temperature anomalies over this region.

This study investigates the impact of temperature and moisture profiles from Atmospheric Infrared Sounder (AIRS) on the prediction of the Indian summer monsoon, using the variational data assimilation system annexed to the Weather Research and Forecasting model. In this study, three numerical experiments are carried out. The first is the control and includes no assimilation; in the second, named Conv, assimilation of conventional Global Telecommunication System data is performed. The third one, named ConvAIRS, is identical to the Conv except that it also includes assimilation of AIRS profiles. The initial fields of tropospheric temperature and water vapor mixing ratio showed significant improvement over the model domain. Assimilation of AIRS profiles has significant impact on predicting the seasonal mean monsoon characteristics such as tropospheric temperature, low‐level moisture distribution, easterly wind shear, and precipitation. The vertical structure of the root‐mean‐square error is substantially affected by the assimilation of AIRS profiles, with smaller errors in temperature, humidity, and wind magnitude. The consequent improved representation of moisture convergence in the boundary layer (deep convection as well) causes an increase in precipitation forecast skill. The fact that the monsoonal circulation is better captured, thanks to an improved representation of thermal gradients, which in turn leads to more realistic moisture transport, is particularly noteworthy. Several previous data impact studies with AIRS and other sensors have focused on the short or medium range of the forecast. The demonstrated improvement in all the predicted fields associated with the Indian summer monsoon, consequent to the month long assimilation of AIRS profiles, is an innovative finding with large implications to the operational seasonal forecasting capabilities over the Indian subcontinent.