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Chandrasekar, V.

Publications and source records attributed to Chandrasekar, V..

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

X-Band Radar and Surface-Based Observations of Cold-Season Precipitation in Western Colorado’s Complex Terrain

Abstract Hydrologic processes associated with intermountain cold-season precipitation in the Upper Colorado River basin have important impacts on avalanche forecasting and water resource management. However, traditional weather radar networks struggle with observations in this complex terrain. Data collected during the Study of Precipitation, the Lower Atmosphere, and the Surface for Hydrometeorology (SPLASH) and its sister campaign, Surface Atmosphere Integrated Field Laboratory (SAIL) in the East River watershed of western Colorado, are used to examine a multistorm period from 23 December 2021 to 1 January 2022 that contributed 35% of the total winter precipitation in this watershed. Dual-polarization X-band radar and disdrometer measurements show ∼30-mm differences in precipitation amount at two sites in proximity over four distinct storm events within the period. Wind patterns, synoptic forcings, microphysical characteristics of precipitation, and surface meteorology are analyzed to explain the observed spatial variability of cold-season precipitation in complex mountainous terrain. Analysis shows that differences over time within this event are mainly accounted for by synoptic forcings, such as frontal passages; differences between sites are accounted for by the impact of variations in local wind patterns on precipitation microphysics. Patterns of surface precipitation intensity are compared and found to be correlated with X-band radar signatures; a relationship between a strong dendritic growth stage and intense low-density surface precipitation is reinforced by this study. This relationship demonstrates the importance of particle growth mechanisms on surface snowfall patterns in high-altitude complex terrain, underscoring the importance of realistic microphysical parameterizations. Significance Statement The amount and density of snowpack from western Colorado winter storms have significant impacts on water resources in the Upper Colorado River basin. Snowpack characteristics are affected by small-scale differences in how snow forms in the atmosphere. These differences are hard to study in the complex terrain of the Rockies, but data from the SPLASH and SAIL field campaigns allows us to investigate how snow crystal formation and mountain-driven wind patterns affect snow near the surface. Our study finds that snow crystal growth varies over small space and time scales and is likely controlled by the terrain beneath a given location and resultant local wind patterns. These results imply that predicting snowpack in the Rockies requires properly representing local wind patterns and crystal growth processes in models.

Heflin, Stella↗
NASA NTRS2018

NASA Observations and Modeling During ICE-POP

Recap: NASA-Specific Objectives for ICE-POP: Provide real-time observational and NWP data in support of ICE-POP, participate in significant international science effort; GPM (Global Precipitation Measurement) Ground Validation and NASA Weather Program -Direct/physical validation of active/passive satellite-based snowfall retrieval algorithms over coastline and mountains; melting layer interaction with terrain -Physics of snow, coupling to snow water equivalent rate and satellite remote sensor retrieval algorithm assumptions - -Size distributions, types/habit, water equivalent, profiles -NU-WRF (NASA-Unified Weather Research and Forecasting) Model plus Observational analyses: Movement toward “level IV products” leverage intensive and multi-faceted NWP (Numerical Weather Prediction) component -Model precipitation processes (liquid, mixed phase and frozen); Build model testing database for further active/passive remote sensing algorithm development (e.g., satellite data simulators) -"Integrated" validation of products in operational context.

Precipitation Science↗
NASA NTRS2018

NASA D3R: 2.0, Enhanced Radar with New Data and Control Features

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) was developed to support development of algorithms and validation for the global precipitation measurement (GPM) missions dual-frequency precipitation radar (DPR). The D3R has participated extensively in various field campaigns in North America with geographic features covering both summer and winter climatic regimes. During the year 2017, D3R went through a major upgrade, specially with the digital receiver and waveform generation subsystems. In this work, the D3R systems upgrade will be discussed with a focus on key features of the new system. The new flexible architecture will enable new research capabilities that will be described.

Kumar, Mohit↗
NASA NTRS2017

Meteorological Observations and System Performance From the NASA D3R's First 5 Years

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) was conceived and developed to support ground validation (GV) operations of the Global Precipitation Measurement (GPM) mission. The D3R operates in the same frequencies bands, Ku- and Ka-band, as GPMs dual-frequency precipitation radar enabling direct comparisons of microphysical observations of precipitation. To support the GPM GVmission, D3R substantively participated in four field campaigns in North America with diverse geographic features covering both winter and summer conditions.

Radar↗
NASA NTRS2017

Microphysical Properties of Snow and Their Link to Ze-S Relations During BAECC 2014

This study uses snow events from the Biogenic Aerosols-Effects on Clouds and Climate (BAECC) 2014 campaign to investigate the connection between properties of snow and radar observations. The general hydrodynamic theory is applied to video-disdrometer measurements to retrieve masses of falling ice particles. Errors associated with the observation geometry and the measured particle size distribution (PSD) are addressed by devising a simple correction procedure. The value of the correction factor is determined by comparison of the retrieved precipitation accumulation with weighing-gauge measurements. Derived mass-dimensional relations are represented in the power-law form m = amDbm. It is shown that the retrieved prefactor am and exponent bm react to changes in prevailing microphysical processes. From the derived microphysical properties, event-specific relations between the equivalent reflectivity factor Ze and snowfall precipitation rate S (Ze = 5 azsSbzs ) are determined. For the studied events, the prefactor of the Ze-S relation varied between 53 and 782 and the exponent was in the range of 1.19-1.61. The dependence of the factors azs and bzs on the m(D) relation and PSD are investigated. The exponent of the Ze-S relation mainly depends on the exponent of the m(D) relation, whereas the prefactor azs depends on both the intercept parameterN0 of the PSDand the prefactors of them(D) andy(D) relations. Changes in azs for a given N0 are shown to be linked to changes in liquid water path, which can be considered to be a proxy for degree of riming.

von Lerber, Annakaisa↗
NASA NTRS2016

Deployment and Performance of the NASA D3R During the GPM OLYMPEx Field Campaign

The NASA D3R was successfully deployed and operated throughout the NASA OLYMPEx field campaign. A differential phase based attenuation correction technique has been implemented for D3R observations. Hydrometeor classification has been demonstrated for five distinct classes using Ku-band observations of both convection and stratiform rain. The stratiform rain hydrometeor classification is compared against LDR observations and shows good agreement in identification of mixed-phase hydrometeors in the melting layer.

Sea measurements↗
NASA NTRS2016

Gpm Level 1 Science Requirements: Science and Performance Viewed from the Ground

GPM meets Level 1 science requirements for rain estimation based on the strong performance of its radar algorithms. Changes in the V5 GPROF algorithm should correct errors in V4 and will likely resolve GPROF performance issues relative to L1 requirements. L1 FOV Snow detection largely verified but at unknown SWE rate threshold (likely < 0.5 –1 mm/hr/liquid equivalent). Ongoing work to improve SWE rate estimation for both satellite and GV remote sensing.

Precipitation↗