Comparison of ITU models performance in predicting Malaysia′s tropical Rainfall Rate and Rain Attenuation at 26GHz mm-wave propagation

Comparative study of the performance of different ITU model in predicting Malaysia tropical climate properties.

Comparison of ITU models performance in predicting Malaysia′s tropical Rainfall Rate and Rain Attenuation at 26GHz mm-wave propagation

Comparative study of the performance of different ITU model in predicting Malaysia tropical climate properties.

Understanding the Climate Behavior Through Data Interpretation: Java-Bali-Nusa Tenggara Case

As an archipelago country in the equator, Indonesia has a tropical climate and often is subjected to monsoonal circulation. The geographical location affects Indonesia to have two seasons, which are the rainy season and drier season. Every season has its characteristic impacts against the mean temperature and rainfall rate. This research aims to analyze Indonesia’s mean temperature and rainfall rate data concerning its tropical climate. The areas observed are limited to Java, Bali, and Nusa Tenggara Island from January 2019 to December 2020. The data gathered from the official Badan Meteorologi Klimatologi dan Geofisika (BMKG) website were processed using MATLAB, and Spearman’s correlation was applied to analyze the rainfall and temperature data. From the observation, this study discovered that the mean temperature data is stable throughout the areas but reaches maximum during the transition between rainy and drier seasons and minimum during the middle of the rainy season. The data observation is often fluctuated, even though showing less rain during the drier season and more during rainy seasons. The fluctuation is affected by the geographical fact that Indonesia has a large water surface, which makes evaporation easily induced by warm tropical temperatures.

Global increase in tropical cyclone rain rate

Theoretical models of the potential intensity of tropical cyclones (TCs) suggest that TC rainfall rates should increase in a warmer environment but limited observational evidence has been studied to test these hypotheses on a global scale. The present study explores the general trends of TC rainfall rates based on a 19-year (1998–2016) time series of continuous observational data collected by the Tropical Rainfall Measuring Mission and the Global Precipitation Measurement mission. Overall, observations exhibit an increasing trend in the average TC rainfall rate of about 1.3% per year, a fact that is contributed mainly by the combined effect of the reduction in the inner-core rainfall rate with the increase in rainfall rate on the rainband region. We found that the increasing trend is more pronounced in the Northwestern Pacific and North Atlantic than in other global basins, and it is relatively uniform for all TC intensities. Further analysis shows that these trends are associated with increases in sea surface temperature and total precipitable water in the TC environment.

Opportunistic Rain Rate Estimation from Measurements of Satellite Downlink Attenuation: A Survey

Recent years have witnessed a growing interest in techniques and systems for rainfall surveillance on regional scale, with increasingly stringent requirements in terms of the following: (i) accuracy of rainfall rate measurements, (ii) adequate density of sensors over the territory, (iii) space-time continuity and completeness of data and (iv) capability to elaborate rainfall maps in near real time. The devices deployed to monitor the precipitation fields are traditionally networks of rain gauges distributed throughout the territory, along with weather radars and satellite remote sensors operating in the optical or infrared band, none of which, however, are suitable for full compliance to all of the requirements cited above. More recently, a different approach to rain rate estimation techniques has been proposed and investigated, based on the measurement of the attenuation induced by rain on signals of pre-existing radio networks either in terrestrial links, e.g., the backhaul connections in cellular networks, or in satellite-to-earth links and, among the latter, notably those between geostationary broadcast satellites and domestic subscriber terminals in the Ku and Ka bands. Knowledge of the above rain-induced attenuation permits the retrieval of the corresponding rain intensity provided that a number of meteorological and geometric parameters are known and ultimately permits estimating the rain rate locally at the receiver site. In this survey paper, we specifically focus on such a type of “opportunistic” systems for rain field monitoring, which appear very promising in view of the wide diffusion over the territory of low-cost domestic terminals for the reception of satellite signals, prospectively allowing for a considerable geographical capillarity in the distribution of sensors, at least in more densely populated areas. The purpose of the paper is to present a broad albeit synthetic overview of the numerous issues inherent in the above rain monitoring approach, along with a number of solutions and algorithms proposed in the literature in recent years, and ultimately to provide an exhaustive account of the current state of the art. Initially, the main relevant aspects of the satellite link are reviewed, including those related to satellite dynamics, frequency bands, signal formats, propagation channel and radio link geometry, all of which have a role in rainfall rate estimation algorithms. We discuss the impact of all these factors on rain estimation accuracy while also highlighting the substantial differences inherent in this approach in comparison with traditional rain monitoring techniques. We also review the basic formulas relating rain rate intensity to a variation of the received signal level or of the signal-to-noise ratio. Furthermore, we present a comprehensive literature survey of the main research issues for the aforementioned scenario and provide a brief outline of the algorithms proposed for their solution, highlighting their points of strength and weakness. The paper includes an extensive list of bibliographic references from which the material presented herein was taken.

Microphysical features of typhoon and non-typhoon rainfall observed in Taiwan, an island in the northwestern Pacific

Information about the raindrop size distribution (RSD) is vital for comprehending the precipitation microphysics, improving the rainfall estimation algorithms, and appraising the rainfall erosivity. Previous research has revealed that the RSD exhibits diversity with geographical location and weather type, which leads to the assessment of the region and weather-specific RSDs. Based on long-term (2004 to 2016) disdrometer measurements in northern Taiwan, this study attempts to demonstrate the RSD aspects of summer seasons that were bifurcated into two weather conditions, namely typhoon (TY) and non-typhoon (NTY) rainfall. The results show a higher concentration of small drops and a lower concentration of large-sized drops in TY compared to NTY rainfall, and this behavior persisted even after characterizing the RSDs into different rainfall rate classes. RSDs expressed in gamma parameters show higher mass-weighted mean diameter (Dm) and lower normalized intercept parameter (Nw) values in NTY than TY rainfall. Moreover, sorting these two weather conditions (TY and NTY rainfall) into stratiform and convective regimes revealed a larger Dm in NTY than in TY rainfall. The RSD empirical relations used in the valuation of rainfall rate (Z–R, Dm–R, and Nw–R) and rainfall kinetic energy (KE–R and KE–Dm) were enumerated for TY and NTY rainfall, and they exhibited profound diversity between these two weather conditions. Attributions of RSD variability between the TY and NTY rainfall to the thermodynamical and microphysical processes are elucidated with the aid of reanalysis, remote sensing, and ground-based data sets.

MSDM v1.0: A machine learning model for precipitation nowcasting over eastern China using multisource data

Eastern China is one of the most economically developed and densely populated areas in the world. Due to its special geographical location and climate, eastern China is affected by different weather systems, such as monsoons, shear lines, typhoons, and extratropical cyclones. In the near future, the rainfall rate becomes difficult to predict precisely due to these systems. Traditional physics-based methods such as numerical weather prediction (NWP) tend to perform poorly on nowcasting problems due to the spin-up issue. Moreover, various meteorological stations are distributed in this region, generating a large amount of observation data every day, which have great potential for application to data-driven methods. Thus, it is important to train a data-driven model from scratch that is suitable for the specific weather situation of eastern China. However, due to the high degrees of freedom and nonlinearity of machine learning algorithms, it is difficult to add physical constraints. Therefore, with the intention of using various kinds of data as a proxy for physical constraints, we collected three kinds of data (radar, satellite, and precipitation data) in the flood season from 2017 to 2018 in this area and preprocessed them into tensors (256×256) that cover eastern China with a domain of 12.8×12.8∘. The developed multisource data model (MSDM) combines the optical flow, random forest, and convolutional neural network (CNN) algorithms. It treats the precipitation nowcasting task as an image-to-image problem, which takes radar and satellite data with an interval of 30 min as inputs and predicts radar echo intensity with a lead time of 30 min. To reduce the smoothing caused by convolutions, we use the optical flow algorithm to predict satellite data in the following 120 min. The predicted radar echoes from the MSDM together with satellite data from the optical flow algorithm are recursively implemented in the MSDM to achieve a 120 min lead time. The MSDM predictions are comparable to those of other baseline models with a high temporal resolution of 6 min. To solve blurry image problems, we applied a modified structural similarity (SSIM) index as a loss function. Furthermore, we use the random forest algorithm with predicted radar and satellite data to estimate the rainfall rate, and the results outperform those of the traditional, nonlinear radar reflectivity factor and rainfall rate (Z–R) relationships that use logarithmic functions. The experiments confirm that machine learning with multisource data provides more reasonable predictions and reveals a better nonlinear relationship between radar echo and precipitation rate. Apart from developing complicated machine learning algorithms, exploiting the potential of multisource data will yield more improvements.

A Short-Term Quantitative Precipitation Forecasting Approach Using Radar Data and a RAP Model

Very short-term (0~3 h) radar-based quantitative precipitation forecasting (QPF), also known as nowcasting, plays an essential role in flash flood warning, water resource management, and other hydrological applications. A novel nowcasting method combining radar data and a model wind field was developed and validated with two hurricane precipitation events. Compared with several existing nowcasting approaches, this work attempts to enhance the prediction capabilities from two major aspects. First, instead of using a radar reflectivity field, this work proposes the use of the rainfall rate field estimated from polarimetric radar variables in the motion field derivation. Second, the derived motion field is further corrected by the Rapid Refresh (RAP) model field. With the corrected motion field, the future rainfall rate field is predicted through a linear extrapolation method. The proposed method was validated using two hurricanes: Harvey and Irma. The proposed work shows an enhanced performance according to statistical scores. Compared with the model only and centroid-tracking only approaches, the average probability of detection (POD) increases about 25% and 50%; the average critical success index (CSI) increases about 20% and 37%; and the average false alarm rate (FAR) decreases about 14% and 16%, respectively.