Rainfall monitoring in Sri Lanka employing commercial microwave links

2021 ◽  
Author(s):  
Aart Overeem ◽  
Hidde Leijnse ◽  
Thomas van Leth ◽  
Linda Bogerd ◽  
Jan Priebe ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Land Surface ◽  
Urban Areas ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Cellular Communication ◽  
Dual Frequency ◽  
Middle Income ◽  
Rain Gauges ◽  
Weather Radars

<p>Microwave backhaul links from cellular communication networks provide a valuable “opportunistic” source of high-resolution space–time rainfall information, complementing traditional in situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Over the past decade, a growing community of researchers has, in close collaboration with cellular communication companies, developed retrieval algorithms to convert the raw microwave link signals, stored operationally by their network management systems, to hydrometeorologically useful rainfall estimates. Operational meteorological and hydrological services as well as private consulting firms are showing an increased interest in using this complementary source of rainfall information to improve the products and services they provide to end users from different sectors, from water management and weather prediction to agriculture and traffic control. The greatest potential of these opportunistic environmental sensors lies in those geographical areas over the land surface of the Earth with few rain gauges and no weather radars: often mountainous and urban areas, but especially low- to middle-income regions, which are generally in (sub)tropical climates. </p><p>Here, the open-source R package RAINLINK is employed to retrieve CML rainfall maps covering the majority of Sri Lanka, a middle-income country having a tropical climate. This is performed for a 3.5-month period based on CML data from on average 1140 link paths. CML rainfall maps are compared locally to hourly and daily rain gauge data, as well as to rainfall maps from the Dual-frequency Precipitation Radar on board the Global Precipitation Measurement Core Observatory satellite. The results confirm the potential of CMLs for real-time tropical rainfall monitoring. This holds a promise for, e.g., ground validation of or merging with satellite precipitation products.</p>

scholarly journals Precipitation Analysis over the French Alps Using a Variational Approach and Study of Potential Added Value of Ground-Based Radar Observations

2017 ◽  
Vol 18 (5) ◽  
pp. 1425-1451 ◽  
Author(s):  
Camille Birman ◽  
Fatima Karbou ◽  
Jean-François Mahfouf ◽  
Matthieu Lafaysse ◽  
Yves Durand ◽  
...  
Keyword(s):  
Snow Depth ◽  
Positive Impact ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Added Value ◽  
Background Information ◽  
Time Step ◽  
Rain Gauges ◽  
Weather Radars ◽  
Precipitation Estimates

Abstract A one-dimensional variational data assimilation (1DVar) method to retrieve profiles of precipitation in mountainous terrain is described. The method combines observations from the French Alpine region rain gauges and precipitation estimates from weather radars with background information from short-range numerical weather prediction forecasts in an optimal way. The performance of this technique is evaluated using measurements of precipitation and of snow depth during two years (2012/13 and 2013/14). It is shown that the 1DVar model allows an effective assimilation of measurements of different types, including rain gauge and radar-derived precipitation. The use of radar-derived precipitation rates over mountains to force the numerical snowpack model Crocus significantly reduces the bias and standard deviation with respect to independent snow depth observations. The improvement is particularly significant for large rainfall or snowfall events, which are decisive for avalanche hazard forecasting. The use of radar-derived precipitation rates at an hourly time step improves the time series of precipitation analyses and has a positive impact on simulated snow depths.

scholarly journals ECLand: The ECMWF Land Surface Modelling System

2021 ◽  
Author(s):  
Souhail Boussetta ◽  
Gianpaolo Balsamo ◽  
Gabriele Arduini ◽  
Emanuel Dutra ◽  
Joe McNorton ◽  
...  
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Weather Prediction ◽  
Open Water ◽  
Surface Fluxes ◽  
Atmospheric Composition ◽  
Flexible System ◽  
Wide Range ◽  
Forecasting System ◽  
The One

The land-surface developments of the European Centre for Medium-range Weather Forecasts (ECMWF) are based on the Carbon-Hydrology Tiled Scheme for Surface Exchanges over Land (CHTESSEL) and form an integral part of the Integrated Forecasting System (IFS), supporting a wide range of global weather, climate and environmental applications. In order to structure, coordinate and focus future developments and benefit from international collaboration in new areas, a flexible system named ECLand which would facilitates modular extensions to support numerical weather prediction (NWP) and society-relevant operational services, e.g. Copernicus, is presented . This paper introduces recent examples of novel ECLand developments on (i) vegetation, (ii) snow, (iii) soil, (iv) open water/lake (v) river/inundation, and (vi) urban areas. The developments are evaluated separately with long-range, atmosphere-forced surface offline simulations, and coupled land-atmosphere-ocean experiments. This illustrates the benchmark criteria for assessing both, process fidelity with regards to land surface fluxes and reservoirs of the water-energy-carbon exchange on the one hand, and on the other hand the requirements of ECMWF’s NWP, climate and atmospheric composition monitoring services using an Earth system assimilation prediction framework.

scholarly journals Time-dependent <i>Z-R</i> relationships for estimating rainfall fields from radar measurements

2010 ◽  
Vol 10 (1) ◽  
pp. 149-158 ◽  
Author(s):  
L. Alfieri ◽  
P. Claps ◽  
F. Laio
Keyword(s):  
Time Estimation ◽  
Rain Gauge ◽  
Rainfall Rate ◽  
Time Step ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Weather Radars ◽  
The North ◽  
Real Time Estimation ◽  
Power Law Equation

Abstract. The operational use of weather radars has become a widespread and useful tool for estimating rainfall fields. The radar-gauge adjustment is a commonly adopted technique which allows one to reduce bias and dispersion between radar rainfall estimates and the corresponding ground measurements provided by rain gauges. This paper investigates a new methodology for estimating radar-based rainfall fields by recalibrating at each time step the reflectivity-rainfall rate (Z-R) relationship on the basis of ground measurements provided by a rain gauge network. The power-law equation for converting reflectivity measurements into rainfall rates is readjusted at each time step, by calibrating its parameters using hourly Z-R pairs collected in the proximity of the considered time step. Calibration windows with duration between 1 and 24 h are used for estimating the parameters of the Z-R relationship. A case study pertaining to 19 rainfall events occurred in the north-western Italy is considered, in an area located within 25 km from the radar site, with available measurements of rainfall rate at the ground and radar reflectivity aloft. Results obtained with the proposed method are compared to those of three other literature methods. Applications are described for a posteriori evaluation of rainfall fields and for real-time estimation. Results suggest that the use of a calibration window of 2–5 h yields the best performances, with improvements that reach the 28% of the standard error obtained by using the most accurate fixed (climatological) Z-R relationship.

scholarly journals What Do Rain Gauges Tell Us about the Limits of Precipitation Predictability?*

2013 ◽  
Vol 26 (15) ◽  
pp. 5682-5688 ◽  
Author(s):  
Dan Gianotti ◽  
Bruce T. Anderson ◽  
Guido D. Salvucci
Keyword(s):  
Land Surface ◽  
Regional Climate ◽  
Rain Gauge ◽  
Potential Predictability ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Climate Forecasting ◽  
The Pacific ◽  
Climate Signals ◽  
Cold Spots

Abstract A generalizable method is presented for establishing the potential predictability for seasonal precipitation occurrence using rain gauge data. This method provides an observationally based upper limit for potential predictability for 774 weather stations in the contiguous United States. It is found that the potentially predictable fraction varies seasonally and spatially, and that on average 30% of year-to-year seasonal variability is potentially explained by predictable climate processes. Potential predictability is generally highest in winter, appears to be enhanced by orography and land surface coupling, and is lowest (stochastic variance is highest) along the Pacific coast. These results depict “hot” spots of climate variability, for use in guiding regional climate forecasting and in uncovering processes driving climate. Identified “cold” spots are equally useful in guiding future studies as predictable climate signals in these areas will likely be undetectable.

scholarly journals Statistical and neural classifiers in estimating rain rate from weather radar measurements

2007 ◽  
Vol 10 ◽  
pp. 111-115
Author(s):  
C. I. Christodoulou ◽  
S. C. Michaelides
Keyword(s):  
Electromagnetic Radiation ◽  
Rain Rate ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Meteorological Conditions ◽  
Swiss Alps ◽  
Rain Gauges ◽  
Weather Radars ◽  
Radar Measurements

Abstract. Weather radars are used to measure the electromagnetic radiation backscattered by cloud raindrops. Clouds that backscatter more electromagnetic radiation consist of larger droplets of rain and therefore they produce more rain. The idea is to estimate rain rate by using weather radar as an alternative to rain-gauges measuring rainfall on the ground. In an experiment during two days in June and August 1997 over the Italian-Swiss Alps, data from weather radar and surrounding rain-gauges were collected at the same time. The statistical KNN and the neural SOM classifiers were implemented for the classification task using the radar data as input and the rain-gauge measurements as output. The proposed system managed to identify matching pattern waveforms and the rainfall rate on the ground was estimated based on the radar reflectivities with a satisfactory error rate, outperforming the traditional Z/R relationship. It is anticipated that more data, representing a variety of possible meteorological conditions, will lead to improved results. The results in this work show that an estimation of rain rate based on weather radar measurements treated with statistical and neural classifiers is possible.

Global NWP Modeling of urban environments at ~1km resolution

2021 ◽  
Author(s):  
Joe McNorton ◽  
Nicolas Bousserez ◽  
Gabriele Arduini ◽  
Anna Agusti-Panareda ◽  
Gianpaolo Balsamo ◽  
...  
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Weather Prediction ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Urban Environments ◽  
Trace Gas Emissions ◽  
Urban Representation ◽  
Forecasting System ◽  
Future Work

&lt;p&gt;Urban areas make up only a small fraction of the Earth&amp;#8217;s surface; however, they are home to over 50% of the global population. Accurate numerical weather prediction (NWP) forecasts in these areas offer clear societal benefits; however, land-atmosphere interactions are significantly different between urban and non-urban environments. Forecasting urban weather requires higher model resolution than the size of the urban domain, which is often achievable by regional but not global NWP models. Here we present the preliminary implementation of an urban scheme within the land surface component of the global Integrated Forecasting System (IFS), at recently developed ~1km horizontal resolution. We evaluate the representation error of fluxes and NWP variables at coarser resolutions (~9 km and ~31 km), using the high resolution as truth. We evaluate the feasibility of the scheme and its urban representation at ~1km scales. Availability of urban mapping data limit the affordable complexity of the global scheme; however, using generalisations model performance is improved over urban sites, even adopting simple schemes, and the modelled Urban Heat Island effects show broad agreement with observations. Several directions for future work are explored including a more complex urban representation, restructuring of the urban tiling and the introduction of an urban emissions model for trace gas emissions.&lt;strong&gt; &lt;/strong&gt;&lt;/p&gt;

scholarly journals Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities

2016 ◽  
Vol 97 (4) ◽  
pp. 621-638 ◽  
Author(s):  
Jian Zhang ◽  
Kenneth Howard ◽  
Carrie Langston ◽  
Brian Kaney ◽  
Youcun Qi ◽  
...  
Keyword(s):  
Weather Prediction ◽  
High Volume ◽  
Radar Data ◽  
Rain Gauge ◽  
Numerical Weather Prediction Model ◽  
Weather Radars ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Environmental Prediction ◽  
Very High

Abstract Rapid advancements of computer technologies in recent years made the real-time transferring and integration of high-volume, multisource data at a centralized location a possibility. The Multi-Radar Multi-Sensor (MRMS) system recently implemented at the National Centers for Environmental Prediction demonstrates such capabilities by integrating about 180 operational weather radars from the conterminous United States and Canada into a seamless national 3D radar mosaic with very high spatial (1 km) and temporal (2 min) resolution. The radar data can be integrated with high-resolution numerical weather prediction model data, satellite data, and lightning and rain gauge observations to generate a suite of severe weather and quantitative precipitation estimation (QPE) products. This paper provides an overview of the initial operating capabilities of MRMS QPE products.

scholarly journals ECLand: The ECMWF Land Surface Modelling System

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 723
Author(s):  
Souhail Boussetta ◽  
Gianpaolo Balsamo ◽  
Gabriele Arduini ◽  
Emanuel Dutra ◽  
Joe McNorton ◽  
...  
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Weather Prediction ◽  
Open Water ◽  
Surface Fluxes ◽  
Atmospheric Composition ◽  
Flexible System ◽  
Wide Range ◽  
Forecasting System ◽  
The One

The land-surface developments of the European Centre for Medium-range Weather Forecasts (ECMWF) are based on the Carbon-Hydrology Tiled Scheme for Surface Exchanges over Land (CHTESSEL) and form an integral part of the Integrated Forecasting System (IFS), supporting a wide range of global weather, climate and environmental applications. In order to structure, coordinate and focus future developments and benefit from international collaboration in new areas, a flexible system named ECLand, which would facilitate modular extensions to support numerical weather prediction (NWP) and society-relevant operational services, for example, Copernicus, is presented. This paper introduces recent examples of novel ECLand developments on (i) vegetation; (ii) snow; (iii) soil; (iv) open water/lake; (v) river/inundation; and (vi) urban areas. The developments are evaluated separately with long-range, atmosphere-forced surface offline simulations and coupled land-atmosphere-ocean experiments. This illustrates the benchmark criteria for assessing both process fidelity with regards to land surface fluxes and reservoirs of the water-energy-carbon exchange on the one hand, and on the other hand the requirements of ECMWF’s NWP, climate and atmospheric composition monitoring services using an Earth system assimilation and prediction framework.

scholarly journals Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

2015 ◽  
Vol 8 (8) ◽  
pp. 8191-8230 ◽  
Author(s):  
A. Overeem ◽  
H. Leijnse ◽  
R. Uijlenhoet
Keyword(s):  
Communication Networks ◽  
Cellular Communication ◽  
Retrieval Algorithm ◽  
Data Set ◽  
Rain Gauges ◽  
Continental Scale ◽  
Weather Radars ◽  
Cellular Communication Network ◽  
Scripting Language ◽  
Areal Rainfall

Abstract. Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide rainfall maps can be derived from the signal attenuations of microwave links in such a network. Here we give a detailed description of the employed rainfall retrieval algorithm and provide the corresponding code. Moreover, the code (in the scripting language "R") is made available including a data set of commercial microwave links. The purpose of this paper is to promote rainfall monitoring utilizing microwave links from cellular communication networks as an alternative or complementary means for global, continental-scale rainfall monitoring.

scholarly journals Precipitation observation using microwave backhaul links in the alpine and pre-alpine region of Southern Germany

2012 ◽  
Vol 9 (1) ◽  
pp. 741-776 ◽  
Author(s):  
C. Chwala ◽  
A. Gmeiner ◽  
W. Qiu ◽  
S. Hipp ◽  
D. Nienaber ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Alpine Region ◽  
New Method ◽  
Small Data ◽  
Near Surface ◽  
Southern Germany ◽  
Rain Gauges ◽  
Weather Radars ◽  
Data Loggers ◽  
Rain Rates

Abstract. Measuring rain rates over complex terrain is afflicted with large uncertainties because rain gauges are influenced by orography and weather radars are mostly not able to look into mountain valleys. We apply a new method to estimate near surface rain rates exploiting attenuation data from commercial microwave links in the alpine region of Southern Germany. Received signal level (RSL) data is recorded minutely with small data loggers at the towers and then sent to a database server via GSM. Due to the large RSL fluctuations in periods without rain, the determination of attenuation caused by precipitation is not straightforward. To be able to continuously process the RSL data from July 2010 to October 2010, we introduce a new method to detect wet and dry periods using spectral time series analysis. We show the performance and limitations of the method and analyse the derived rain rates compared to rain gauge and weather radar measurements. The resulting correlations differ for different links and reach values of R2 = 0.80 for the link-gauge comparison and R2 = 0.84 for the link-radar comparison.

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