A Simplified Algorithm to Estimate Latent Heating Rate Using Vertical Rainfall Profiles Over the Tibetan Plateau

Abstract

In this study, a simplified semiphysical retrieval algorithm for latent heat (LH) released from precipitation over the Tibetan Plateau is derived and analyzed. The physical basis of this algorithm is that the vertical gradient of rain rate (−dR/dZ or Γ) represents the temporal rate of rain formation based on the steady state assumption, and the precipitation formation rate is closely related to the cloud formation rate, which is directly proportional to the latent heating rate. In this algorithm, the LH rate is represented as a linear function of Γ with fixed slope and intercept term determined by 3‐month Weather Research and Forecasting Model simulations over the Tibetan Plateau. Comparison to model results shows that the retrieval scheme can correctly capture the main features of LH horizontally and vertically. Comparison with results from other two widely accepted LH algorithms using Global Precipitation Measurement Dual Precipitation Radar real observations shows that this retrieval scheme generally agrees with them over low‐altitude areas but yields more convective‐type LH over the highlands with a relatively low heating center. This algorithm is specially designed for application to high altitudes. With this algorithm and the associated coefficients provided, researchers can readily do LH retrieval in their cases of interest by themselves. The only required input is the vertical profile of rain rate, which is available from current satellite precipitation radar observations.

Impact of topography on black carbon transport to the southern Tibetan Plateau during the pre-monsoon season and its climatic implication

“…The enhanced valley wind across the Himalayas has also been found by previous studies with observations and numerical simulations (e.g., Egger et al, 2000;Zängl et al, 2001;Carrera et al, 2009;Karki et al, 2017;Lin et al, 2018). However, it is noteworthy that previous studies have found that the orographic drag (including gravity wave drag and turbulence orographic form drag) over a region with complex topography, such as the Himalayas and other mountainous areas, would weaken the overall near-surface wind speed (e.g., Beljaars et al, 2004;Horvath et al, 2012;Jiménez and Dudhia, 2012;Zhou et al, 2017Zhou et al, , 2018Lin et al, 2018;Wang et al, 2020). Therefore, the near-surface wind speed is also examined.…”

“…The goal of this study is to investigate the impacts of different representations of topography on the transport of BC across the Himalayas. Therefore, besides this control experiment, one sensitivity (idealized) experiment is also conducted with the same configuration as the control one except that the terrain heights of the inner domain at 4 km resolution are bilinearly interpolated from the terrain heights at 20 km resolution similar as previous studies (e.g., Shi et al, 2008;Wu et al, 2012b;Lin et al, 2018). The two exper- iments are referred to the simulations with the complex and smooth topographies, respectively, hereafter.…”

“…The model is suitable for simulations at hydrostatic and non-hydrostatic scales and thus can be used for investigating the impacts of resolutiondependent features, such as topography, on modeling results. In particular, the meteorological part of the model (WRF) has been systematically evaluated and used to investigate the impacts of resolutions on simulations of moisture transport and climate over the Himalayan region (e.g., Shi et al, 2008;Karki et al, 2017;Lin et al, 2018;Zhou et al, 2017Zhou et al, , 2018Wang et al, 2020). All of these previous studies with the model lay the foundation for this modeling study.…”

Trumpet‐Shaped Topography Modulation of the Frequency, Vertical Structures, and Water Path of Cloud Systems in the Summertime Over the Southeastern Tibetan Plateau: A Perspective of Daytime–Nighttime Differences

“…Note that the warm and humid summer monsoon frequently reaches the steep southern slope of the QTP, making it conducive to the formation of unique cloud–precipitation distributions (Chen et al, ; Chen et al, ; Fu et al, ; Qie et al, ; Yan et al, ; Yan & Liu, ; Zhang et al, ). In particular, the southeastern QTP (SETP) exhibits high‐frequency convective cloud systems consisting of very high cloud cover and large‐areal precipitation (Kurosaki & Kimura, ; Li et al, ; Maussion et al, ; Tan et al, ; Tong et al, ; Yu & Fu, ), and is the source of many large rivers in Asia, including the Jinsha, Lancang, and Nujiang rivers (Tan et al, ). In addition, 26% of convective cloud systems over the QTP move eastward in summer (Hu et al, ), leading to frequent storm rainfall and floods in summer over eastern China (Tao & Ding, ; Yasunari & Miwa, ; Zhao et al, ).…”

“…The analysis of observations from active and passive satellite instrumentation has enabled significant progress in the quantitative understanding of the horizontal and vertical spatial distribution of cloud systems over the southern QTP (Qie et al, ; Chen et al, ; Fu et al, ; Zhang et al, ; Chen et al, ; Yan & Liu, ; Luo et al, ; Li et al, ). For instance, Chen et al () revealed that the distribution of cloud top heights (CTH) gradually changes from bimodal (3 and 15 km) over the foothills of the Himalayas, to unimodal (7–9 km) over the QTP.…”