Identifying and Characterizing Tropical Oceanic Mesoscale Cold Pools using Spaceborne Scatterometer Winds

Abstract

Convective cold pools over oceans contribute to the air‐sea exchange of heat and moisture. Beyond cold pools' physical properties, cold pools and their intersections are important mechanisms controlling the life cycle of tropical convective systems. However, few observational techniques are available to routinely observe the properties of cold pools and their association with convection. The primary goal of this study is to demonstrate a new technique using gradients in remotely sensed ocean vector winds as a proxy for the structure and characteristics of tropical oceanic cold pools. Using Advanced Scatterometer (ASCAT) vector wind retrievals with field of view of 12.5 km, regions of enhanced gradients in surface winds are identified as gradient features. A 3‐km Weather Research and Forecasting (WRF) model simulation was carried out to illustrate the technique over the Indian Ocean during the 2011 DYNAmics of Madden‐Julian Oscillation (DYNAMO) field campaign. A forward operator was applied to the model wind fields, allowing direct comparisons of wind gradients and virtual temperature‐defined cold pool thermodynamic and kinematic properties. In situ buoy measurements of air temperature were also used to validate ASCAT‐identified cold pools. Quantitative skill metrics demonstrate that the technique has a low false alarm rate (<10%), high critical success index (>85%), and a low bias (~1). The gradient feature technique was then applied to ASCAT‐retrieved ocean vector winds, revealing global variations in cold pool properties. These properties are shown to be related to the properties of global convective systems observed by the Tropical Rainfall Measurement Mission (TRMM).

Evaluating the Detection of Mesoscale Outflow Boundaries Using Scatterometer Winds at Different Spatial Resolutions

“…The detection of outflow boundaries is based on the algorithm developed by Garg et al [23]. The mathematical expression of the wind gradient is:…”

“…The perimeter of a set of triangles was formed implementing alpha shapes, identified as a concave hull object, termed as 'Gradient Feature' (GF). Garg et al [23] carried out simulations using the Weather Research and Forecasting Model over the tropical Indian Ocean for a portion of the DYNAmics of Madden-Julian Oscillation (DYNAMO) field campaign period, in order to create a physical basis behind choosing the thresholds for the wind gradient.…”

“…The spatial resolution of the UHR product is a factor of about 2 less than the standard L2W product, therefore additional tuning of the GF algorithm [23] was performed. To note, both primary and secondary thresholds are set an order of magnitude less than in the standard product.…”

“…The primary and secondary thresholds used in the GF algorithm were 6.8 × 10 −5 s −1 and 1.2 × 10 −4 s −1 , respectively. Garg et al [23] evaluated the GF method using thresholds determined by a model simulation. Based on statistical indices-i.e., critical success index, false alarm and bias ratio-the authors obtained a value of 6.8 × 10 −5 s −1 and 1.2 × 10 −4 s −1 for the primary and secondary thresholds, respectively.…”

“…Leveraging wind retrievals at 12.5-km spatial resolution from the ASCAT instruments onboard the Meteorological Operational-A, -B (Metop-A, -B) satellite series, Garg et al [23] developed a new method to identify cold pools globally based on the horizontal wind gradient, termed as 'Gradient Feature' (GF). The validity of this method was evaluated with simulation of the Weather Research and Forecasting (WRF) model over the Indian Ocean during the DYNAmics of Madden-Julian Oscillation field campaign, as well as air temperature from buoys and precipitation data from the Tropical Rainfall Measurement Mission (TRMM).…”