Investigation of features of May, 2001 tropical cyclone over the Arabian Sea through IRS-P4 and other satellite data

In this paper, utility of satellite derived atmospheric motion vectors and geophysical parameters is brought out to discern appropriate signals for improving short-range forecasts in respect of development/dissipation of tropical cyclones over the Indian region. Results of a particular case study of May, 2001 cyclone, which formed in the Arabian Sea are reported. Analysis of wind field with input of modified cloud motion vectors and water vapour wind vectors is performed utilizing Optimum Interpolation (OI) technique at 850 and 200 hPa for finding dynamical changes such as vorticity, convergence and divergence for the complete life period of this cyclone. Simultaneously, variations in geophysical parameters obtained from IRS-P4 and TRMM satellites in ascending and descending nodes are compared with dynamical variations for discerning some positive signals to improve short range forecasts over the Indian region. The enhancement of cyclonic vorticity at 200 hPa over larger area surrounding center of cyclone was observed from 26 to 28 May 2001 which gave a positive signal for dissipation of storm.

Towards seamless irradiance nowcasting on intrahour to intraday scales using all-sky and satellite imagery

<p align="justify">Photovoltaics (PV) power generation depends on atmospheric conditions and especially cloudiness causes strong fluctuations. Accurate nowcasting of the cloud situation and resulting solar irradiance for the next minutes and hours is thus essential for successful integration of PV power plants into the electricity grid. Ground based all-sky imagers allow for spatial and temporal high resolution snapshots of the cloud situation at the position of the plant at low costs. However, the limited area of the sky visible limits the possible forecast time when using cloud motion extrapolation. Commonly used consumer grade cameras further on do not easily allow for elaborate retrieval of cloud optical characteristics necessary for quantitative irradiance forecasts. In contrast, Meteosat satellite data allows for large spatial coverage and estimation of cloud optical properties suitable for intraday nowcasting but with lower spatial and temporal resolution. Due to the different resolutions, joint beneficial usage of all-sky and satellite imagery as well as smooth transitions between all-sky and satellite based nowcasting are challenging.</p> <p align="justify">This work demonstrates the possibility of supplementary usage of satellite imagery for all-sky image based intrahour nowcasting using a simplified 4D-Var assimilation framework. The use of satellite imagery also allows for a seamless transition from intrahour to intraday irradiance nowcasting.</p> <p align="justify">For the intrahour period, all-sky images from a pair of imagers are evaluated to obtain stereo cloud heights, cloud motion vectors and cloud masks utilizing a synthetic clearsky library. This data is combined into a 2D cloud mask advection model using a simplified 4D-var assimilation scheme. Data from multiple imagers and timesteps as well as satellite derived irradiance maps can be put to use within the assimilation framework and as boundary condition.<br />Within this work, satellite based irradiance maps are computed using methods based on Sirch (2018). Cloud optical properties are retrieved from Meteosat Second Generation (MSG) images with the APICS (Bugliaro et al. 2011) and CiPS (Strandgren et al., 2017) algorithms. These are horizontally advected using two separate layers for thin cirrus and thicker clouds, which are then input to radiative transfer calculations. Resulting irradiance maps can be used for the intrahour assimilation as well as seamlessly following intraday nowcasts.</p> <p align="justify"> </p> <p align="justify">References:</p> <p>Sirch, Tobias (2018): Multi-resolution nowcasting of clouds and DNI with MSG/SEVIRI for an optimized operation of concentrating solar power plants. Dissertation, LMU München: Faculty of Physics</p> <p>Bugliaro, L., Zinner, T., Keil, C., Mayer, B., Hollmann, R., Reuter, M., and Thomas, W.: Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI, Atmos. Chem. Phys., 11, 5603–5624,2011.</p> <p align="justify">Strandgren, J., Bugliaro, L., Sehnke, F., and Schröder, L.: Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks, Atmos. Meas. Tech., 10, 3547–3573, 2017.</p>

Erste Analysen, Charakterisierungen räumlich verteilter Einstrahlungsdaten gemessen mit einer fliegenden Messplattform (Quadcopter Icaros #2)

<p>In 2021 wurde der Funktionsnachweis ('proof of concept') zu einer fliegenden Messplattform vorgestellt. Dazu wurde für einen Quadrocopter eine kleine Wetterstation ausgelegt und aufgebaut. Diese Wetterstation vermittelt ihre Daten in Realzeit an eine Bodenstation, die diese Daten auswertet, Reaktionen ermöglicht und die Daten in eine Datenbank schreibt. Die Drohne mit ihrem Messaufbau (Icaros #1) zeigte auf den Flugstecken befriedigende Flugeigenschaften. Der Nachweis, dass eine Datenkommunikation von der Drohne zu einer Datenbankinstanz im Internet zuverlässig und in Realzeit funktioniert, wurde erbracht. Die Testflüge zeigten, dass physikalische Messdaten den Rasterpunkten eines beflogenen Gebietes eindeutig zuordenbar sind. Identifizierte Verbesserungsmöglichkeiten wurden in einer Weiterentwicklung der fliegenden Messplattform in dem Prototypen Icaros 2 umgesetzt. Orientiert am Pollentransport bei Bienen wurde ein neuer Aufbau versucht. Das Gehäuse (leichterer 3D-Druck) und die Platinen (Verkleinerung) wurden modifiziert. Die Messplattform wurde modular konzipiert und kann auch mit weiteren Sensoren und ASI-Kameras erweitert werden. Die Flugeigenschaften verbesserten sich deutlich. Mit der Drohne steht nun eine hoch-flexible und responsive Messplattform zur Verfügung. Zunächst wurden Flugrouten in Orientierung an Cloud Motion Vektoren optimiert. Die Messflüge bei verschiedener Bewölkung erlauben erste Grafiken und Auswertungen zur räumlichen Einstrahlungsverteilung. An der Charakterisierung der räumlichen Einstrahlungsverteilung wird aktuell gearbeitet. Auf Basis der Matrizen wird die räumliche Modellierung von PV-Systemen möglich. Parallel laufen Arbeiten an Icaros #3.</p>

Impact of Tropical Convective conditions on Solar Irradiance Forecasting based on Cloud Motion Vectors

<p>Cloud Motion Vector (CMV) estimation from consecutive satellite images is widely used commercially for providing hours-ahead intraday forecasts of solar irradiance and PV power production. The modelling assumptions in these methods are generally satisfied for advective motion which is common in mid-latitudes, but strained for tropical meteorological conditions dominated by convective clouds. The region under analysis in this study encompasses both tropical and sub-tropical climatic zones and is affected by seasonal strong convection, i.e., the South Asian Monsoon.</p> <p>The purpose of this study is to benchmark the monthly forecast error of three commonly used CMV estimation techniques - Block-match, Farnebäck (Optical flow) and TV-L<sup>1</sup> (Optical flow), for analysing their performance on a seasonal basis. The main focus of this work is the analysis of the limitations of image processing based Block-match and Optical flow techniques in predicting irradiance during the Monsoon period, which presents frequent convective formation and dissipation.</p> <p>Forecasted Cloud Index (CI) maps are validated against reference analysis CI maps for the period 2018-2019 for forecast lead times from 0 to 5.5 hours ahead using the Peak Signal to Noise Ratio (PSNR) metric for estimating the accuracy. Persistence of analysis cloud index maps are used as the reference worst case scenario forecast. Site-level forecasts of irradiance for the same period are validated against ground measured irradiance from two BSRN stations - Gurgaon and Tiruvallur, located in Northern and Southern India respectively.</p> <p>From the Winter period in March to the Monsoon period in August, there is a marked reduction of the 30 minutes ahead forecast accuracy by 3 dB in terms of Peak Signal to Noise Ratio at the image-wide level. This can be observed for all the three methods and the worst-case persistence scenario. Both optical flow methods outperform Block-match by 0.5 dB for the entire period of analysis. The Gurgaon BSRN site is affected by Summer Monsoon and shows an increase in nRMSE by a factor of 3 for all the methods. This station shows a seasonal pattern of forecast error closely matching the image-wide forecast accuracy. The forecast error for the Tiruvallur BSRN station on the other hand reaches its peak in December (Data for October and November are absent), due to its location in the Winter Monsoon climatic zone. Again, the nRMSE for all methods increase by a factor of almost 3 from March to December. The inter-method difference in accuracy is not significant and a seasonal difference (20% nRMSE) dominates. This highlights the shortcomings of image processing techniques in extrapolating future cloud locations under convective situations, where there is rapid change in cloud content between consecutive images.</p>

The State-of-the-Art Progress in Cloud Detection, Identification, and Tracking Approaches: A Systematic Review

A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too.

Impact of KALPANA-1 cloud motion vector in the analysis and forecast of IMD operational NWP system

lkj & 12 flrEcj 2002 ls ekSle foKku ds fy, lefiZr Hkkjr ds igys mixzg dYiuk-1 ds fØ;k’khy gksus ds lkFk gh fgan egklkxj ds vuqiyC/k vk¡dM+s okys {ks= ds mixzg ls izkIr iouksa ds vk¡dM+ksa ds {ks= foLrkj ls lq/kkj gqvk gS A bu vk¡dM+ksa ds miyC/k gks tkus ls Hkkjr ekSle foKku foHkkx ¼Hkk-ekS-fo-fo-½ dh izpkyukRed ,u- MCY;w- ih- iz.kkyh esa buds izHkko dh tk¡p djus dh ,d ubZ laHkkouk mRiUu gqbZ gS A bl ’kks/k Ik= esa o"kZ 2003 dh ekulwu o"kkZ ij fd, x, iz;ksxksa ds vk/kkj ij Hkkjr ekSle foKku foHkkx dh ,u- MCY;w- ih- iz.kkyh esa dYiyk-1 ls izkIr gq, lh- ,e- oh- vk¡dM+ksa ds izHkko ds ckjs esa crk;k x;k gS A bl fun’kZ ls izkIr gq, iou ds vfrfjDr vk¡dM+ksa dk izHkko lkFkZd vkSj ykHkdkjh ik;k x;k gS A The coverage of satellite derived winds over the data gap Indian Ocean region has improved with the operation of India’s first dedicated satellite for meteorology KALPANA-1 since 12 September 2002. Availability of these data has opened up a new possibility to examine the impact of these data in the operational NWP system of India Meteorological Department (IMD). In this paper, impact of KALPANA-1 CMV data in the NWP system of IMD has been presented based on the experiments carried-out for the monsoon 2003. The impact of additional wind data in the model is found to be significant and beneficial.

A Method of Ground-Based Cloud Motion Predict: CCLSTM + SR-Net

Ground-based cloud images can provide information on weather and cloud conditions, which play an important role in cloud cover monitoring and photovoltaic power generation forecasting. However, the cloud motion prediction of ground-based cloud images still lacks advanced and complete methods, and traditional technologies based on image processing and motion vector calculation are difficult to predict cloud morphological changes. In this paper, we propose a cloud motion prediction method based on Cascade Causal Long Short-Term Memory (CCLSTM) and Super-Resolution Network (SR-Net). Firstly, CCLSTM is used to estimate the shape and speed of cloud motion. Secondly, the Super-Resolution Network is built based on perceptual losses to reconstruct the result of CCLSTM and, finally, make it clearer. We tested our method on Atmospheric Radiation Measurement (ARM) Climate Research Facility TSI (total sky imager) images. The experiments showed that the method is able to predict the sky cloud changes in the next few steps.