Assessing Historical Variability of South Asian Monsoon Lows and Depressions With an Optimized Tracking Algorithm

Abstract

Cyclonic low‐pressure systems (LPS) produce abundant rainfall in South Asia, where they are traditionally categorized as monsoon lows, monsoon depressions, and more intense cyclonic storms. The India Meteorological Department (IMD) has tracked monsoon depressions for over a century, finding a large decline in their number in recent decades, but their methods have changed over time and do not include monsoon lows. This study presents a fast, objective algorithm for identifying monsoon LPS and uses it to assess interannual variability and trends in reanalyses. Variables and thresholds used in the algorithm are selected to best match a subjectively analyzed LPS data set while minimizing disagreement between four reanalyses in a training period. The stream function of 850 hPa horizontal wind is found to be optimal in this sense; it is less noisy than vorticity and represents the complete nondivergent wind, even when flow is not geostrophic. Using this algorithm, LPS statistics are computed for five reanalyses, and none show a detectable trend in monsoon depression counts since 1979. Both the Japanese 55‐year Reanalysis (JRA‐55) and the IMD data set show a step‐like reduction in depression counts when they began using geostationary satellite data, in 1979 and 1982, respectively; the 1958–2018 linear trend in JRA‐55, however, is smaller than in the IMD data set, and its error bar includes 0. There are more LPS in seasons with above‐average monsoon rainfall and in La Niña years, but few other large‐scale modes of interannual variability are found to modulate LPS counts, lifetimes, or track length consistently across reanalyses.

Influence of Intraseasonal Variability on the Development of Monsoon Depressions

“…Therefore, our period of analysis is 1998-2012, the common period of data availability for all data sets. However, we also test the robustness of our results using a recently developed LPS data set that was compiled with an optimized algorithm that tracks 850 hPa stream function anomalies in the ERA5 reanalysis (Vishnu et al, 2020), together with the TRMM Multisatellite Precipitation Analysis daily accumulated rainfall product (https://disc.gsfc.nasa.gov/datasets/TRMM_3B42_Dai-ly_7/summary;%201998-2019) and interpolated outgoing longwave radiation (OLR;1979-2018 (Liebmann & Smith, 1996); provided by the NOAA/OAR/ESRL PSL (Boulder, Colorado, USA).…”

Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1

“…1 shows a screenshot of this information as it is presented to the expert contributors. Times are chosen randomly within the years 2008 and 2009, which were chosen to correspond to the time period associated with the Year of Tropical Convection (Waliser et al, 2012). The interface allows a user to enumerate ARs within a given field by clicking the mouse in the vicinity of an AR.…”

TempestExtremes v2.1: A Community Framework for Feature Detection, Tracking and Analysis in Large Datasets

“…Monsoonal lows and depressions: Analogous to the study of Zarzycki and Ullrich (2017), Vishnu et al (2020) optimized DetectNodes for tracking of monsoon lows and depressions. A comprehensive analysis of input fields found that 850hPa streamfunction tended to produce better results compared with trackers based on sea level pressure, vorticity, and geopotential.…”

“…Even so, work to identify optimal tracking criteria continues (Murata et al, 2019). Beyond these traditionally tracked features, many recent papers have focused on defining regionally-relevant features such as monsoonal lows and depressions, associated with heavy precipitation in monsoonal regions (Hurley and Boos, 2015;Vishnu et al, 2020). Areal feature tracking algorithms have also been developed for clouds (Heikenfeld et al, 2019), atmospheric rivers (Shields et al, 2018;Rutz et al, 2019), atmospheric blocking (Scherrer et al, 2006), mesoscale convective systems (Prein et al, 2017;Feng et al, 2018), precipitation clusters (Clark et al, 2014;Pendergrass et al, 2016), and frontal systems (Hope et al, 2014;Schemm et al, 2015;Parfitt et al, 2017).…”