A method for convective storm detection using satellite data

Abstract

RESUMENLa humedad y la inestabilidad, en conjunto con un mecanismo de disparo, son las principales claves de iniciación y evolución de las tormentas de convección profunda. Los datos satelitales pueden proporcionar mediciones indirectas de la inestabilidad y la humedad de una amplia área en cortos periodos de tiempo. En este trabajo se estudia la utilización de un método objetivo basado en el uso combinado de técnicas de estimación de convección basadas en satélites. Este método se fundamenta en diferentes técnicas dispuestas en un enfoque multicapa de diferentes características convectivas, con el objetivo de estratificar un tope nuboso. Se investigan los canales infrarrojo (IR) de 10.8 μm y de vapor de agua de 6.2 μm de Meteosat segunda generación (MSG) junto con la temperatura de la tropopausa proporcionada por un modelo numérico. Se aplican el umbral, diferencias de brillo de temperatura y tendencias en el tiempo a la información disponible, de lo cual resulta un producto de cinco capas que destaca las áreas de diferentes actividades de convección. Este método de estratificación mostró gran capacidad para evaluar mejor la convección fuerte en comparación con las técnicas más simples como IR de falso color, y fue especialmente eficiente para identificar la célula de convección fuerte en un área grande con varios focos convectivos. Se llevó a cabo un análisis de validación utilizando datos de radar y de rayos, lo cual demuestra que este enfoque es muy útil para distinguir los casos fuertes de los débiles desde las primeras horas mediante la selección de patrones convectivos sutiles solamente presentes en tormentas severas. Los pequeños cambios en la evolución de la tormenta también se apreciaron mejor en los resultados arrojados por este método, lo cual facilita su identificación. Además se observaron algunas incertidumbres, probablemente debido al gran ángulo de visión, lo cual demuestra que la técnica derivada de las bandas espectrales del MSG tiene buena precisión para el estudio de los grandes sistemas convectivos en la región austral de Sudamérica. ABSTRACTMoisture and instability, along with a triggering mechanism, are the main keys of deep convective storms initiation and evolution. Satellite data can provide indirect measurements of instability and moisture of a wide area in short periods of time. This paper studies the use of an objective method based on a blended use of multiple satellite-based convection estimation techniques. This method is based on different techniques arranged in a several layers approach of different convective features, aiming to stratify a cloud shield. Meteosat Second Generation ( together with tropopause temperature information provided by a numerical model. Threshold, brightness temperature differences (BTD), and time trends are applied to the information available resulting in a five layers product, highlighting areas of different convective activities. This cloud shield stratification method showed a great ability to better evaluate strong convection when compared with s...

Analysis of Livorno Heavy Rainfall Event: Examples of Satellite-Based Observation Techniques in Support of Numerical Weather Prediction

“…To this end, a case study related to airplane accident caused by a not correctly forecast deep convection event is analyzed. In [11], infrared and water vapor wavelengths geostationary observations are explored together with tropopause temperature information provided by an NWP model, to implement a method that aims to stratify a cloud shield to better characterize and track the evolution of convective clouds. Other works [12,13] propose the CMORPH (Climate Prediction Center morphing) method, that uses motion vectors derived from geostationary satellite infrared data to propagate forward in space and time the precipitation measurements retrieved by passive microwave (PMW) radiometers, in order to obtain global precipitation estimates at 8 km of spatial resolution every half hour.…”

The New Potential of Deep Convective Clouds as a Calibration Target for a Geostationary UV/VIS Hyperspectral Spectrometer

“…Thus, in this study, the pixels in rows 41-48 are also eliminated, which are possibly affected by the RA effect but which are not flagged as such (https://projects.knmi.nl/omi/research/product/rowanomaly-messages.php). As shown in Figure 5b, with the elimination of the measurements in the CCD rows close to the nadir (41)(42)(43)(44)(45)(46)(47)(48), the mean reflectivity becomes much closer to the DCC reflectivity of the TROPOMI. Because the rows close to the nadir port generally have a low viewing zenith angle, which satisfies the angle condition for DCC detection, the RA effect significantly influences the availability of DCC observations from the OMI.…”

“…Here, the optical depth τ λ (z) is estimated using the approximation suggested by Bodhaine et al [46] that considers the altitude and Rayleigh scattering in the atmosphere. The cloud altitude is set to approximately 16 km because the cloud top of DCCs nearly reaches the tropopause in the equatorial region [31,47]. This means that the optical depth of the atmosphere above the clouds is within the range of 0.0005-0.0025 from 300 to 500 nm.…”

Remote Sensing Observations of Thunderstorm Features in Latvia

“…Associated to the vertical extent of convective clouds up to the lower stratosphere is the occurrence of overshooting tops and gravity waves that can be identified from satellite measurements in the visible part of the spectrum. While overshooting tops occur because of cloud top penetration through the tropopause, gravity waves form and propagate outward when the cloud top oscillates vertically about the level of neutral buoyancy [25]- [27]. Another indicator used for severe thunderstorm detection was a value exceeding 45 obtained from the brightness temperature difference (hereafter BTD) of the channels IR 3.9 and IR 10.8.…”

“…In addition, a great proportion of scientific research studies has focused on the benefits of using algorithms and products obtained from meteorological satellite data. For instance, algorithms for the detection of rapidly developing thunderstorms [23], characteristics of the observed cloud top temperatures (hereafter CTT) [20], [24], [25] and visual cloud top features [26], [27] have been found to be good indicators for thunderstorm severity and could be used as practical tools for the analysis and nowcasting of thunderstorm events.…”