Rainy Clouds Research Articles

A novel fuzzy based deep neural network for rain fall prediction using cloud images

SummaryThe most challenging aspect of weather forecasting is predicting rainfall. In contrast, timely and precise rainfall forecasting is immensely useful in developing effective security measures for active infrastructure projects, logistics, agricultural activities, air transport operations, and flood scenarios. Artificial intelligence has been established to predict rain precipitation, but very little research has been conducted on images than time‐series data to predict rainfall. However, the accuracy of the prediction model utilizing pictures is lower when compared to time series data. Thus a novel fuzzy based deep neural network (NFDNN) model was created to increase rainfall prediction performance. Initially, our research utilized input cloud images, in which the fuzzy wavelet incorporated with neural networks is utilized to identify clouds in the sky. Second, the types of clouds are determined by a fuzzy deep neural networks cloud classifier in which the adaptive fuzzy sparse representation is used. Then, the density‐based spatial clustering for applications containing noise clustering method was used to detect rainy clouds by evaluating the cloud's color and density from the fuzzy deep neural network cloud classifier. Finally, rainfall was predicted using information such as cloud categorization, appearance, and height obtained from cloud position and cloud types. As a result, the proposed NFDNN model gives a better prediction result than the existing methods.

Impacts and variability on the urban climate of Recife – Pernambuco, Brazil

The objective of this work was to identify monthly and annual oscillations and trends in meteorological variables: maximum and minimum air temperatures, precipitation, and relative humidity, number of rainy days, total sunshine, wind intensity and cloud cover. We used the theoretical probability distribution: Weibull, Log-normal and Logistics to adjust the values ​​of the variables mentioned above. The Kolmogorov-Smirnov test was used to verify the adjustment of the theoretical functions. The study of the spatial temporal behavior of rainfall and monthly, maximum and minimum temperatures, relative humidity, insolation, cloud cover and wind used was acquired from the National Institute of Meteorology, the data period comprising the series from 1962 to 2015. For The determination of the theoretical distributions of probability adjusted to the annual extremes of precipitation, maximum and minimum temperature of the air, total isolation and relative humidity, cloud cover and wind intensity, were used the maximum likelihood methods to estimate the parameters of the distributions. The Kolmogorov-Smirnov test was used to compare the fit and select the best theoretical distributions. The adjustments were also evaluated in graphs. Deforestation of native vegetation for the construction of districts, favelas and buildings above six floors, as well as high burnings, has contributed to the high rates of desertification, silting up rivers, streams, streams, wells, ponds, ponds and lagoons. Water table, causing extreme fluctuations in the contribution of meteorological elements and well-being in urban centers. The Weibull and Logistics distributions were the best fit for precipitation, insolation, relative humidity, minimum temperature and cloud coverage.

Fundamental Aspects of Vedic Sciences by Yajna Vijnan and Mantra Therapy

The Present manuscript discusses on various scientific aspects of Indian Vedic Agnihotra Vijnan and Mantra therapy. It has been scientifically proven in many recent experiemnts and literature that Homa therapy is much effective in inviting rainy clouds, efficient in disesaes control through inhaling therapy, generates negative ions responsible for happiness, organic homa krishi (farming) is best for humans, boon for mental and physical fitness. The authors’ team have tried best to present a series of small experiemnts in support of few of above results and confident enough that gradually this therapy is poular and being accepted globally by one and all. Indian culture and science is scientific and full of components to uplift the human conciousness and ease in life. The present study supports this fact by visualizations and sensor based experiemnts. In 21st century, it is crucial to accept with open minded the good features of this alternate therapy in view of second and third and multiple waves of pandemic caused by sars-cov’19 and other global threats.

Integrated Knowledge in The Pre-Islamic Era:'Anwa'' Meteorologıcal Observations and Rain's Prediction as a Model

Some scholars are still thinking that Arabs in Pre-Islamic era were immersed in ignorance, and they had no knowledge and sciences to help them wade through their tough life. Those scholars even believe that Pre-Islamic Arabs confined themselves with a life of passiveness, negativity and nihilism. This paper aims to demonstrate a scientific refutation of the above allegation by proving that Arabs were dealing at high level with every facet of their environment. The researcher will specify his defense of this case, by taking one issue as a model which is The Rain and its predictions. Abundant poems have mentioned rain and rainfalls in their odes. Moreover, in the documentation era, a lot of books have been authored and appeared, which are called Anwa'“; i.e. meteorological observations. Some of such books are published nowadays, while many are still kept as manuscripts or have been lost. I concluded that Arabs have dealt with the predictions of rains and established rubrics in exploring the rainy clouds. They knew the times of rainfalls by the clouds' colors and lightening. In addition, there were persons and tribes who were the most knowing in this regard. Another conclusion in this paper, worth mentioning is that Arabs have achieved integrated Knowledge, and this paper represents a response to those who defame the Pre-Islamic Era and its people as being satisfied with eloquence, orations and poets. Furthermore, I found out that Arabs have linked up with other countries and benefited from their knowledge and was not isolated in the Arabic Peninsula.

Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation.

Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images

Precipitation, especially convective precipitation, is highly associated with hydrological disasters (e.g., floods and drought) that have negative impacts on agricultural productivity, society, and the environment. To mitigate these negative impacts, it is crucial to monitor the precipitation status in real time. The new Advanced Baseline Imager (ABI) onboard the GOES-16 satellite provides such a precipitation product in higher spatiotemporal and spectral resolutions, especially during the daytime. This research proposes a deep neural network (DNN) method to classify rainy and non-rainy clouds based on the brightness temperature differences (BTDs) and reflectances (Ref) derived from ABI. Convective and stratiform rain clouds are also separated using similar spectral parameters expressing the characteristics of cloud properties. The precipitation events used for training and validation are obtained from the IMERG V05B data, covering the southeastern coast of the U.S. during the 2018 rainy season. The performance of the proposed method is compared with traditional machine learning methods, including support vector machines (SVMs) and random forest (RF). For rainy area detection, the DNN method outperformed the other methods, with a critical success index (CSI) of 0.71 and a probability of detection (POD) of 0.86. For convective precipitation delineation, the DNN models also show a better performance, with a CSI of 0.58 and POD of 0.72. This automatic cloud classification system could be deployed for extreme rainfall event detection, real-time forecasting, and decision-making support in rainfall-related disasters.

Estimation of activity the methane seepage from the Black Sea floor using MODIS images and geosciences data

The publication presents the results of a certain stage of the study: the features of methane gas seepage characteristics in the transition zone of shelf-continental slope in the region of the Dnieper Paleodelts in the northwestern part of the Black Sea are identified, taking into account the data set: information of satellite images and of geological-geophysical (acoustic) data. About 2000 of the MODIS satellite images (product MOD021KM) are acquired in stages over one year period. The image processing was carried out in the GIS environment. It was established that the volume of methane gas seepage on the investigated site is fixed on satellite images and is most pronounced during increased seismotectonic activity (activation of earthquakes in the Black Sea region) - this is due to the tectonic structure of the study area, which is in the regional plan on the edge of the Eastern European platform, and in the local area, is the Dnepr paleo-delta in the zone of decomposition, which is an additional factor for gas outlets here. On satellite images small clouds are observed in cloudy weather or absence of clouds on the background of continuous clouds. This is due to the physical properties of methane, which is (according to geochemical studies) about 99% of the released gas. Methane rising from the bottom of the sea to the surface, and then to the troposphere is much colder than the surface water and warmer than the atmosphere layer, which provokes the formation of condensate over the site with methane gas seepage, and when the weather conditions change, forming a rainy cloud with a sharp drop in temperature, the volume of methane gas of seepage up are much warmer than clouds, forming a cloudy area on a background of continuous clouds. This phenomenon is recorded in various spectral (thermal and infrared) bands in satellite images (NASA – MODIS, NOAA, etc.), methane rising to the lower layers of the atmosphere intensively absorbs thermal radiation of the Earth in the infrared spectral region at a wavelength of 7.66 µm. It enables to determine the methane gas seepage in satellite images for future studies of gas outlets from the bottom of the sea, taking into account the geological information and additional hydrological and weather data. The conducted monitoring of the satellite images showed that during the period of increased seismic activity in this region (in particular under the influence of the zone Vranch), it directly affects the tectonic situation in the northwestern part of the Black Sea and increases the emissions of methane gases from the bottom of the sea, which are then recorded on the MODIS sensor.

Performance of CMORPH, TMPA, and PERSIANN rainfall datasets over plain, mountainous, and glacial regions of Pakistan

The present study aims at the assessment of six satellite rainfall estimates (SREs) in Pakistan. For each assessed products, both real-time (RT) and post adjusted (Adj) versions are considered to highlight their potential benefits in the rainfall estimation at annual, monthly, and daily temporal scales. Three geomorphological climatic zones, i.e., plain, mountainous, and glacial are taken under considerations for the determination of relative potentials of these SREs over Pakistan at global and regional scales. All SREs, in general, have well captured the annual north-south rainfall decreasing patterns and rainfall amounts over the typical arid regions of the country. Regarding the zonal approach, the performance of all SREs has remained good over mountainous region comparative to arid regions. This poor performance in accurate rainfall estimation of all the six SREs over arid regions has made their use questionable in these regions. Over glacier region, all SREs have highly overestimated the rainfall. One possible cause of this overestimation may be due to the low surface temperature and radiation absorption over snow and ice cover, resulting in their misidentification with rainy clouds as daily false alarm ratio has increased from mountainous to glacial regions. Among RT products, CMORPH-RT is the most biased product. The Bias was almost removed on CMORPH-Adj thanks to the gauge adjustment. On a general way, all Adj versions outperformed their respective RT versions at all considered temporal scales and have confirmed the positive effects of gauge adjustment. CMORPH-Adj and TMPA-Adj have shown the best agreement with in situ data in terms of Bias, RMSE, and CC over the entire study area.

Intermediate Bronze Age in Southern Levant (4200–4000 BP) – Why Did Four Cities in Transjordan Survive Urban Collapse?

Abstract The first urban culture of southern Levant collapsed and the first period of urbanisation of Canaan (Early Bronze Age I-III) terminated at around 4200 yrs BP. The Canaanites abandoned their walled cities, dispersed and underwent pastoralisation. However, the urban centres of southern Canaan were not destroyed. This fact may point to responsibility of the environmental factor and makes influence influence of anthropogenic factors uncertain, along with the most popular Amorite invasion/destruction hypothesis. A tremendous climatic change occurred at that time in many regions, affecting cultures and civilisations of the Ancient Near East and resulting in abandonment of cities, migrations and great civilizational changes. In southern Levant, virtually all cities were left in ruins with a mysterious exception in Transjordan where four cities: Aroer, Ader, Khirbet Iskander and Iktanu survived and existed throughout the period. Most probably when climatic conditions in Cisjordan excluded possibility of urban life, the ones in Transjordan conditions remained unchanged or altered in a very limited scale. It is now clear that after a period with quite humid and warm climate, the precipitation greatly diminished after 4200 yrs BP in a littoral zone of eastern Mediterranean. A part of Transjordan, probably due to presence of the Dead Sea that somehow created conditions that influenced precipitation, remained a climatic niche with decent rainfall that enabled concentration of population in and around big urban centres and continuation of urban civilisation. Warming in a littoral zone changed dew point temperature preventing formations of clouds above western slopes of Judean and Samarian Hills. Moist air, prevented from condensation was transported eastwards where it could reach ascending currents appearing over the Dead Sea. Masses of air with water vapour moving upwards could form rainy clouds in Transjordan.

A SURVEY AND ANALYSIS OF THE FEREYDOUN CHARACTER; BASED ON TRIFUNCTIONAL HYPOTHESIS DUE TO COMPARISON OF THE AVESTAN AND PAHLAVI TEXTS; AND FERDOWSI’S SHAHNAMEH

The present study aimed to review the prominent character of Fereydūn in ancient Iranian literature with an emphasis on the evidence, sentences and expression from the Avesta written in Avestan language as well as the original middle Persian works in Pahlavi language and to compare and analyze them with some of the poems in the Shahnameh that were consistent with Fereydūn character in ancient texts. In this study, according to trifunctional hypothesis, it was found that the cattle and its symbols in the myth of Fereydūnhas played an important role in the social structure of the Indo-Iranian agriculture-oriented tribes, since the herds of cattle has been closely associated with agriculture and cultivation. Among these tribes, the aspect of wāstaryōsīh θraētaona is more predominant than other aspects in the trifunctional hypothesis. While the θraētaona in another manifestation of the aspects (i.e., in the position of ARTĒSTĀR”) has reached this quality and aspect of his character to the peak by crushing down the powerful dragon (He is described as a monster serpent bodied, three headed and six eyed. But in other respects Aži Dahāka has human qualities, and is never a mere animal) and resulting the victorious end. Nevertheless, according to the repeated Yashts in the Avesta, this Yazata aimed to take savang havacha and arənavāci (simple of rainy clouds and lactating cows) back from the bondag of the dragon of the drought

Thresholding Based Method for Rainy Cloud Detection with NOAA/AVHRR Data by Means of Jacobi Itteration Method

Thresholding based method for rainy cloud detection with NOAA/AVHRR data by means of Jacobi iteration method is proposed. Attempts of the proposed method are made through comparisons to truth data which are provided by Japanese Meteorological Agency: JMA which is derived from radar data. Although the experimental results show not so good regressive performance, new trials give some knowledge and are informative. Therefore, the proposed method suggests for creation of new method for rainfall area detection with visible and thermal infrared imagery data.

Estimating the vertical profiles of cloud water content in warm rain clouds

AbstractThe cloud water content (CWC) in rainy clouds is a crucial parameter to determine the onset and the growth rate of precipitation and to quantify the associated latent heating rate. No direct retrieval of CWC in rainy cloud from satellite observations is reported due to the difficulties of separating cloud particles from precipitation‐sized particles. However, based on multiple cloud simulations from the Weather and Research Forecasting (WRF) model, we have found that the CWC profile in warm rains can be well determined by three macrophysical cloud properties of cloud water path (CWP), cloud top height (CTH), and cloud bottom height (CBH). The CBH can be estimated using CWP, CTH, and near‐surface rain rate. We proposed an algorithm with a look‐up table for estimating the CWC profile using CWP, CTH, and near‐surface rain rate as inputs. The performance of this algorithm was tested with WRF model simulations and a real drizzle case observed by the CloudSat satellite. Testing verified that the algorithm can retrieve the vertical distribution of CWC correctly with few errors at different spatiotemporal scales. In addition, the algorithm is not confined to particular microphysics schemes and is valid for multiple cloud systems in different areas over the world. This algorithm is expected to improve current knowledge of cloud water content in rainy clouds.

A new methodology for pixel-quantitative precipitation nowcasting using a pyramid Lucas Kanade optical flow approach

Short-term high-resolution Quantitative Precipitation Nowcasting (QPN) has important implications for navigation, flood forecasting, and other hydrological and meteorological concerns. This study proposes a new algorithm called Pixel-based QPN using the Pyramid Lucas-Kanade Optical Flow method (PPLK), which comprises three steps: employing a Pyramid Lucas-Kanade Optical Flow method (PLKOF) to estimate precipitation advection, projecting rainy clouds by considering the advection and evolution pixel by pixel, and interpolating QPN imagery based on the space-time continuum of cloud patches. The PPLK methodology was evaluated with 2338 images from the geostationary meteorological satellite Fengyun-2F (FY-2F) of China and compared with two other advection-based methods, i.e., the maximum correlation method and the Horn-Schunck Optical Flow scheme. The data sample covered all intensive observations since the launch of FY-2F, despite covering a total of only approximately 10 days. The results show that the PPLK performed better than the algorithms used for comparison, demonstrating less time expenditure, more effective cloud tracking, and improved QPN accuracy. (C) 2015 Elsevier B.V. All rights reserved.

Applicability of satellite rainfall estimates for erosion studies in small offshore areas: A case study in Cape Verde Islands

To assess the usability of satellite rainfall estimates for erosion studies in Cape Verde, the 3B42 and 3B43 products from TRMM, and the Multi-Sensor Precipitation Estimate MPE from Meteosat are compared to daily and monthly ground rainfall measured between 1998 and 2010. TRMM estimates from 1998 and MPE from 2009 were studied and it was found that they detect the general trend and direction of rainy clouds but underestimate the amount of rainfall compared to rain gauges. To obtain an improved rainfall estimate from Meteosat data, the cloud top temperature derived from the 10.8μm infrared channel was correlated to rainfall intensity measured at a 3-minute interval between 2008 and 2010 by a Parsivel optical disdrometer. A modified exponential equation of daily rainfall intensity as a function of cloud top temperature was obtained. The equation was applied to 15-minute intervals from the infrared channel to obtain 15‐minute rainfall and summed up as daily rainfall. The daily rainfall depths estimated from cloud top temperature showed a better correspondence with the amounts measured by ground gauges on the island (r=0.75), and are a first approach to cope with lack of short duration rainfall data for erosion studies in island states such as Cape Verde.

Classification of convective and stratiform rain based on the spectral and textural features of Meteosat Second Generation infrared data

This paper aimed to investigate the potential of using spectral and textural features in the Meteosat Second Generation—Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) data for developing techniques ca- pable of classifying convective and stratiform rain areas in the satellite image. Two different classification methods were introduced that use the brightness temperature (BT) T10.8 and brightness temperature differences T10.8-T12.1, T8.7-T10.8, T6.2-T10.8, T6.2-T7.3, T13.4-T10.8, T8.7-T12.1, and T9.7-T13.4 as spectral parameters, along with textural param- eters derived from the thermal infrared MSG-SEVIRI chan- nel to discriminate between convective and stratiform rainy clouds. The first is an algorithm based on the probability of convective rainfall (PCR) for each pixel of the MSG satellite data and the second is an artificial neural network multilayer perceptron (MLP) model that relies on the correlation of satellite data with convective and stratiform rain. Both schemes were trained using as reference convective/strati- form classification data from 88 stations in Greece for 20 rainy days with high convective activity and evaluated against an independent dataset of gauge data for six rainy days. Two PCR and two MLP algorithms were constructed based on the previous results: the PCR1 and MLP1 algo- rithms that use only spectral measures and the PCR2 and MLP2 algorithms based on both spectral and textural meas- ures. It was found that the introduction of textural parame- ters as additional information to a technique (PCR2 and MLP2 ) works in improving the delineation between convective and stratiform rainy pixels compared to the techniques based only on spectral information (PCR1 and MLP1). The comparison of the two schemes revealed a clear outperformance of the MLP techniques over the PCR tech- niques. Best skill is provided by MLP2 (POD074.5 %, FAR044.3 %, POFD022.5 %, CSI00.47, ETS00.31, bias01.34) followed by MLP1 and the two PCR techniques. All algorithms overestimate the convective rain occurrences observed by the rain gauge network. These findings showed that the combined use of spectral and textural features in the MSG-SEVIRI imagery can be beneficial for the classifica- tion of convective and stratiform precipitating clouds.

Short range tracking of rainy clouds by multi-image flow processing of X-band radar data

Two innovative algorithms for motion tracking and monitoring of rainy clouds from radar images are proposed. The methods are generalizations of classical optical flow techniques, including a production term (modelling formation, growth or depletion of clouds) in the model to be fit to the data. Multiple images are processed and different smoothness constraints are introduced. When applied to simulated maps (including additive noise up to 10 dB of SNR) showing formation and propagation of objects with different directions and velocities, the algorithms identified correctly the production and the flow, and were stable to noise when the number of images was sufficiently high (about 10). The average error was about 0.06 pixels (px) per sampling interval (ΔT) in identifying the modulus of the flow (velocities between 0.25 and 2 px/ΔT were simulated) and about 1° in detecting its direction (varying between 0° and 90°). An example of application to X-band radar rainfall rate images detected during a stratiform rainfall is shown. Different directions of the flow were detected when investigating short (10 min) or long time ranges (8 h), in line with the chaotic behaviour of the weather condition. The algorithms can be applied to investigate the local stability of meteorological conditions with potential future applications in nowcasting.

Observing precipitation by means of water vapor absor tion lines: a first check of the retrieval capabilities of the 183-WSL rain retrieval method

A new approach is described to infer rain rates by using the opaque channels of the Advanced Microwave Sounding Unit-B (AMSU-B) sensor on board of the NOAA satellites. The retrieval scheme of the 183-WSL (Water Strong Lines at 183.31 GHz) algorithm consists of two main modules. First, some thresholds act to discriminate between rainy and non-rainy pixels (183-WSLW). This is necessary to prevent false alarms mainly caused by the strong absorption of the condensed cloud droplets developing during light precipitation formation. Cloud droplets, especially when they feed precipitation formation mechanisms, can sensibly grow as a consequence of intense condensation in the surrounding regions of rainy clouds. Secondly, on the basis of different extinction of radiation, rain types are classified as convective (183-WSLC module) or stratiform (183-WSLS module) typologies.

Evaluating the Utility of Multispectral Information in Delineating the Areal Extent of Precipitation

Abstract Data from geosynchronous Earth-orbiting (GEO) satellites equipped with visible (VIS) and infrared (IR) scanners are commonly used in rain retrieval algorithms. These algorithms benefit from the high spatial and temporal resolution of GEO observations, either in stand-alone mode or in combination with higher-quality but less frequent microwave observations from low Earth-orbiting (LEO) satellites. In this paper, a neural network–based framework is presented to evaluate the utility of multispectral information in improving rain/no-rain (R/NR) detection. The algorithm uses the powerful classification features of the self-organizing feature map (SOFM), along with probability matching techniques to map single- or multispectral input space into R/NR maps. The framework was tested and validated using the 31 possible combinations of the five Geostationary Operational Environmental Satellite 12 (GOES-12) channels. An algorithm training and validation study was conducted over the conterminous United States during June–August 2006. The results indicate that during daytime, the visible channel (0.65 μm) can yield significant improvements in R/NR detection capabilities, especially when combined with any of the other four GOES-12 channels. Similarly, for nighttime detection the combination of two IR channels—particularly channels 3 (6.5 μm) and 4 (10.7 μm)—resulted in significant performance gain over any single IR channel. In both cases, however, using more than two channels resulted only in marginal improvements over two-channel combinations. Detailed examination of event-based images indicate that the proposed algorithm is capable of extracting information useful to screen no-rain pixels associated with cold, thin clouds and identifying rain areas under warm but rainy clouds. Both cases have been problematic areas for IR-only algorithms.

Warm rainy clouds and droplet size distribution

This paper presents the analysis of cloud droplet size distribution (DSD) and the use of a proxy variable for the parameters of the DSD to explore the possibility of detecting warm rainy clouds. Cloud DSD plays a paramount role in the parameterization of cloud microphysics in climate models. The droplet size distribution also plays a crucial role in determining the radiative properties of clouds and is usually obtained using satellite retrieval algorithms. This paper presents an analysis of the modified Gamma model which is used to represent the cloud DSD. The analysis of the DSD is conducted through the derivation of the distribution moments and their physical interpretation. The analysis includes the discussion of the mean, variance, effective radius, and effective variance of the DSD. Usually the effective radius is extracted from radiance measurements. However, since the modified Gamma distribution has two parameters it requires estimates of both the effective radius and effective variance to properly estimate cloud microphysics from radiance information.

Radar analysis of cloud systems and their rainfall yield in Israel

This study documents the climatological occurrence of rainfall contributed from different types of rain cloud systems over Israel and the adjacent sea. The rainy cloud types are: a. Cold front. b. Cloud systems that develop in the cold sector of the cyclones. c. Cloudiness of the cyclone center (vortex). The cloud systems within the cold sector include: a. Convection lines. b. Open Benard cells and unorganized cells. c. Coastal front. d. Cloud streets. While the warm front rain yield is negligible, the Red Sea trough adds about 5% to the total precipitation in the Mediterranean climate region of the country. The main rainfall yield contribution in the north of the country comes from cold fronts and vortices while in the southern part is dominated by less dynamically and more air mass convection controlled rain cloud systems, such as Benard cells and coastal front. Analysis of the cloud pattern on a monthly basis reveals that the cold front is more active during November-December and the main vortex activity starts in January, with the climax of the coastal front in December. We checked rainy/dry year variations and found that during rainy years, the vortex rain contribution is larger than that of the cold front. The coastal front also contributes more rain during wet years and tends to follow a more southerly route. In dry years, the proportion of vortex/cold front is reversed, and the coastal front rain yield is meager and has a more northerly route.