squall lines
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Author(s):  
Sophie Abramian ◽  
Caroline Muller ◽  
Camille Risi
Keyword(s):  

Author(s):  
A. C. Sousa ◽  
L. A. Candido ◽  
P. Satyamurty

AbstractMesoscale convective cloud clusters develop and organize in the form of squall lines along the coastal Amazon in the afternoon hours and propagate inland during the evening hours. The frequency, location, organization into lines and movement of the convective systems are determined by analyzing the “precipitation features” obtained from the TRMM satellite for the period 1998-2014. The convective clusters and their alignments into Amazon coastal squall lines are more frequent from December through July and they mostly stay within 170 km from the coast line. Their development and movement in the afternoon and evening hours of about 14 m s-1 are helped by the sea breeze. Negative phase of Atlantic Dipole and La Niña combined increase the frequency of convective clusters over coastal Amazon. Composite environmental conditions of 13 large Amazon coastal squall line cases in April show that conditional instability increases from 09 LT to 12 LT and the wind profiles show a jet like structure in low levels. The differences in the vertical profiles of temperature and humidity between the large squall line composites and no-squall line composites are weak. However, appreciable increase in the mean value of CAPE from 09 LT to 15 LT is found in large squall line composite. The mean mixing ratio of mixed layer at 09 LT in La Niña situations is significantly larger in the large squall line composite. Thus, CAPE and mixed layer mixing ratio are considered promising indicators of the convective activity over the coastal belt of the Amazon Basin.


2021 ◽  
Author(s):  
Sophie Abramian ◽  
Caroline Muller ◽  
Camille Risi
Keyword(s):  

MAUSAM ◽  
2021 ◽  
Vol 71 (2) ◽  
pp. 175-186
Author(s):  
PUVIARASAN N ◽  
YADAV RAMASHRAY ◽  
GIRI R K ◽  
SINGH VIRENDRA

Remote sensing by ground based GPS receivers provide continuous and accurate measurement of precipitable water (PW) of an order of 1.5 mm comparable to radiosondes and water vapour radiometers.  In the present work we have examined the amount of PW variation in three thunderstorms accompanied with rain shower that occurred over the GPS station.  In all the three thunderstorms event heavy rain was reported.  However on comparison of observed rainfall with GPS estimated precipitable water (hourly) in real time, it is observed that among the three, in one event the amount of precipitable water (PW) is much less (~20mm) for the same amount of rainfall.  After analysing and taken into account various source of error, we suspect that in a mesoscale thunderstorms or squall lines associated with heavy rainfall, discrepancies arise because the wet mapping functions that used to map the wet delay at any angle to the zenith do not represent the localized atmospheric condition particularly for narrow towering thunder clouds and non-availability of GPS satellites in the zenith direction.  On the other hand for the larger thunder cells the atmosphere is very nearly azimuthally symmetric with respect to GPS receiver, the error due to the wet mapping function is minimal.


Author(s):  
Fan Wu ◽  
Kelly Lombardo

AbstractA mechanism for precipitation enhancement in squall lines moving over mountainous coastal regions is quantified through idealized numerical simulations. Storm intensity and precipitation peak over the sloping terrain as storms descend from an elevated plateau toward the coastline and encounter the marine atmospheric boundary layer (MABL). Storms are most intense as they encounter the deepest MABLs. As the descending storm outflow collides with a moving MABL (sea breeze), surface and low-level air parcels initially accelerate upward, though their ultimate trajectory is governed by the magnitude of the negative non-hydrostatic inertial pressure perturbation behind the cold pool leading edge. For shallow MABLs, the baroclinic gradient across the gust front generates large horizontal vorticity, a low-level negative pressure perturbation, and thus a downward acceleration of air parcels following their initial ascent. A deep MABL reduces the baroclinically-generated vorticity, leading to a weaker pressure perturbation and minimal downward acceleration, allowing air to accelerate into a storm’s updraft.Once storms move away from the terrain base and over the full depth of the MABLs, storms over the deepest MABLs decay most rapidly, while those over the shallowest MABLs initially intensify. Though elevated ascent exists above all MABLs, the deepest MABLs substantially reduce the depth of the high-θe layer above the MABLs and limit instability. This relationship is insensitive to MABL temperature, even though surface-based ascent is present for the less cold MABLs, the MABL thermal deficit is smaller, and convective available potential energy (CAPE) is higher.


2021 ◽  
Author(s):  
Divya S Vidyadharan ◽  
Aaron Xavier ◽  
Blossom Treesa Bastian ◽  
Ajay Ragh ◽  
Naveen Chittilapilly

<div>Radar-based precipitation nowcasting refers to predicting rain for a short period of time using radar reflectivity images. For dynamic nowcasting, motion fields can be extrapolated using an approximate and localized reduced-order model. Motion field estimation based on traditional Horn-Schunck (HS) algorithm suffers from over-smoothing at discontinuities in non-rigid and dissolving texture present in precipitation nowcasting products. An attempt to preserve the discontinuities using an L1 norm formulation in HS led to the use of Total Variation L1 norm (TVL1). In this paper, we propose a radar-based precipitation nowcasting model with TVL1-based estimation of motion field and Sparse Identification of Non-linear Dynamics (SINDy)-based</div><div>estimation of non-linear dynamics. TVL1 is effective in preserving the edges especially in the case of the eye of typhoons and squall lines while estimating motion vectors. SINDy captures the non-linear dynamics and generates the subsequent update values for the motion field based on a reduced-order representation. Finally, the SINDy-generated ensemble of motion field is used along with</div><div>the radar reflectivity image for generating precipitation nowcasts. We evaluated the effectiveness of TVL1 in preserving edges while capturing the motion field from non-rigid surfaces. The performance of the proposed TVL1-SINDy model in nowcasting weather events such as Typhoons and Squall lines are evaluated using performance metrics such as Mean Absolute Error (MAE), and Critical Success Index (CSI). Experimental results show that the proposed nowcasting system demonstrates better performance compared to the benchmark nowcasting models with lower MAE, higher CSI at higher lead times.</div>


2021 ◽  
Author(s):  
Divya S Vidyadharan ◽  
Aaron Xavier ◽  
Blossom Treesa Bastian ◽  
Ajay Ragh ◽  
Naveen Chittilapilly

<div>Radar-based precipitation nowcasting refers to predicting rain for a short period of time using radar reflectivity images. For dynamic nowcasting, motion fields can be extrapolated using an approximate and localized reduced-order model. Motion field estimation based on traditional Horn-Schunck (HS) algorithm suffers from over-smoothing at discontinuities in non-rigid and dissolving texture present in precipitation nowcasting products. An attempt to preserve the discontinuities using an L1 norm formulation in HS led to the use of Total Variation L1 norm (TVL1). In this paper, we propose a radar-based precipitation nowcasting model with TVL1-based estimation of motion field and Sparse Identification of Non-linear Dynamics (SINDy)-based</div><div>estimation of non-linear dynamics. TVL1 is effective in preserving the edges especially in the case of the eye of typhoons and squall lines while estimating motion vectors. SINDy captures the non-linear dynamics and generates the subsequent update values for the motion field based on a reduced-order representation. Finally, the SINDy-generated ensemble of motion field is used along with</div><div>the radar reflectivity image for generating precipitation nowcasts. We evaluated the effectiveness of TVL1 in preserving edges while capturing the motion field from non-rigid surfaces. The performance of the proposed TVL1-SINDy model in nowcasting weather events such as Typhoons and Squall lines are evaluated using performance metrics such as Mean Absolute Error (MAE), and Critical Success Index (CSI). Experimental results show that the proposed nowcasting system demonstrates better performance compared to the benchmark nowcasting models with lower MAE, higher CSI at higher lead times.</div>


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