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

2016 ◽  
Vol 9 (5) ◽  
pp. 2425-2444 ◽  
Author(s):  
Aart Overeem ◽  
Hidde Leijnse ◽  
Remko Uijlenhoet
Keyword(s):  
Communication Networks ◽  
Cellular Communication ◽  
Retrieval Algorithm ◽  
Data Set ◽  
Continental Scale ◽  
Weather Radars ◽  
Cellular Communication Network ◽  
Scripting Language ◽  
Areal Rainfall ◽  
User Friendly

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 (≈  35 500 km2) 15 min rainfall maps can be derived from the signal attenuations of approximately 2400 microwave links in such a network. Here we give a detailed description of the employed rainfall retrieval algorithm. Moreover, the documented, modular, and user-friendly code (a package in the scripting language “R”) is made available, including a 2-day data set of approximately 2600 commercial microwave links from the Netherlands. The purpose of this paper is to promote rainfall mapping utilising microwave links from cellular communication networks as an alternative or complementary means for continental-scale rainfall monitoring.

scholarly journals Validation of satellite OPEMW precipitation product with ground-based weather radar and rain gauge networks

2013 ◽  
Vol 6 (3) ◽  
pp. 4279-4312 ◽  
Author(s):  
D. Cimini ◽  
F. Romano ◽  
E. Ricciardelli ◽  
F. Di Paola ◽  
M. Viggiano ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Winter Season ◽  
National Research Council ◽  
Residual Effect ◽  
Temporal Analysis ◽  
Mean Difference ◽  
Absolute Difference ◽  
Data Set ◽  
Rain Gauges ◽  
Weather Radars

Abstract. The Precipitation Estimation at Microwave Frequencies (PEMW) algorithm was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) for inferring surface rain intensity (sri) from satellite passive microwave observations in the range from 89 to 190 GHz. The operational version of PEMW (OPEMW) has been running continuously at IMAA-CNR for two years, producing sri estimates feeding an operational hydrological model for forecasting flood alerts. This paper presents the validation of OPEMW against simultaneous ground-based observations obtained by a network of 20 weather radars and a network of more than 3000 rain gauges distributed over the Italian peninsula and main islands. The validation effort uses a data set spanning a one-year period (July 2011–June 2012). The effort evaluates dichotomous and continuous scores for the assessment of rain detection and quantitative estimate, respectively, investigating both spatial and temporal features. The analysis demonstrates 98% accuracy in correctly identifying rainy and non-rainy areas, and it quantifies the increased ability (with respect to random chance) to detect rainy and non-rainy areas (0.42–0.45 Heidke skill score) or rainy areas only (0.27–0.29 equitable threat score). Performances are better than average during summer, fall, and spring, while worse than average in the winter season. The spatial-temporal analysis does not show seasonal dependence except for larger mean absolute difference over the Alps and northern Apennines during winter, attributable to residual effect of snow cover. A binned analysis in the 0–15 mm h−1 range suggests that OPEMW tends to slightly overestimate sri values below 6–7 mm h−1, and to underestimate sri above those values. Depending upon the ground reference (either rain gauges or weather radars), the mean difference is 0.8–2.8 mm h−1, with a standard deviation within 2.6–3.1 mm h−1 and correlation coefficient within 0.8–0.9. The monthly mean difference was shown to remain within ±1 mm h−1 with respect to rain gauges and within −2 mm h−1 with respect to weather radars, with 2–4 mm h−1 standard deviation.

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 Validation of satellite OPEMW precipitation product with ground-based weather radar and rain gauge networks

2013 ◽  
Vol 6 (11) ◽  
pp. 3181-3196 ◽  
Author(s):  
D. Cimini ◽  
F. Romano ◽  
E. Ricciardelli ◽  
F. Di Paola ◽  
M. Viggiano ◽  
...  
Keyword(s):  
Winter Season ◽  
National Research Council ◽  
Weather Radar ◽  
Temporal Analysis ◽  
Skill Score ◽  
Rain Gauge ◽  
Set Covering ◽  
Data Set ◽  
Rain Gauges ◽  
Weather Radars

Abstract. The Precipitation Estimation at Microwave Frequencies (PEMW) algorithm was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) for inferring surface rain intensity (sri) from satellite passive microwave observations in the range from 89 to 190 GHz. The operational version of PEMW (OPEMW) has been running continuously at IMAA-CNR for two years. The OPEMW sri estimates, together with other precipitation products, are used as input to an operational hydrological model for flood alert forecast. This paper presents the validation of OPEMW against simultaneous ground-based observations from a network of 20 weather radar systems and a network of more than 3000 rain gauges distributed over the Italian Peninsula and main islands. The validation effort uses a data set covering one year (July 2011–June 2012). The effort evaluates dichotomous and continuous scores for the assessment of rain detection and quantitative estimate, respectively, investigating both spatial and temporal features. The analysis demonstrates 98% accuracy in correctly identifying rainy and non-rainy areas; it also quantifies the increased ability (with respect to random chance) to detect rainy and non-rainy areas (0.42–0.45 Heidke skill score) or rainy areas only (0.27–0.29 equitable threat score). Performances are better than average during summer, fall, and spring, while worse than average in the winter season. The spatial–temporal analysis does not show seasonal dependence except over the Alps and northern Apennines during winter. A binned analysis in the 0–15 mm h−1 range suggests that OPEMW tends to slightly overestimate sri values below 6–7 mm h−1 and underestimate sri above those values. With respect to rain gauges (weather radars), the correlation coefficient is larger than 0.8 (0.9). The monthly mean difference and standard deviation remain within ±1 and 2 mm h−1 with respect to rain gauges (respectively −2–0 and 4 mm h−1 with respect to weather radars).

scholarly journals Rainfall retrieval algorithm for commercial microwave links: stochastic calibration

2021 ◽  
Author(s):  
Wagner Wolff ◽  
Aart Overeem ◽  
Hidde Leijnse ◽  
Remko Uijlenhoet
Keyword(s):  
The Netherlands ◽  
Communication Networks ◽  
Daily Rainfall ◽  
Estimation Algorithm ◽  
Optimization Method ◽  
Cellular Communication ◽  
Retrieval Algorithm ◽  
General Interest ◽  
Radar Rainfall ◽  
Commercial Microwave Links

Abstract. During the last decade, rainfall monitoring using signal level data from commercial microwave links (CMLs) in cellular communication networks has been proposed as a complementary way to estimate rainfall for large areas. Path-averaged rainfall is retrieved between the transmitting and receiving cellular antenna of a CML. One rainfall estimation algorithm for CMLs is RAINLINK, which has been employed in different regions (e.g., Brazil, Italy, the Netherlands, and Pakistan) with satisfactory results. However, the RAINLINK parameters have been calibrated for a unique optimum solution, which is inconsistent with the fact that multiple similar or equivalent solutions may exist due to uncertainties in algorithm structure, input data, and parameters. Here, we show how CML rainfall estimates can be improved by calibrating all parameters of the algorithm systematically and simultaneously with the stochastic optimization method Particle Swarm Optimisation, which is used for the numerical maximization of the objective function. An open dataset of approximately 2,800 sub-links of minimum and maximum received signal levels over 15-minute intervals covering the Netherlands (~35,500 km2) is employed, where 12 days are used for calibration and 3 months for validation. A gauge-adjusted radar rainfall dataset is utilized as reference. Verification of path-average daily rainfall shows a reasonable improvement for the stochastically calibrated parameters with respect to RAINLINK's default parameter settings. Results further improve when averaged over the Netherlands. Moreover, the method provides a better underpinning of the chosen parameter values and is therefore of general interest for calibration of RAINLINK's parameters for other climates and cellular communication networks.

scholarly journals Total column water vapour measurements from GOME-2 MetOp-A and MetOp-B

2014 ◽  
Vol 7 (3) ◽  
pp. 3021-3073 ◽  
Author(s):  
M. Grossi ◽  
P. Valks ◽  
D. Loyola ◽  
B. Aberle ◽  
S. Slijkhuis ◽  
...  
Keyword(s):  
Water Vapour ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Vertical Column ◽  
Data Set ◽  
Vertical Column Density ◽  
Data Processor ◽  
Climate Analysis ◽  
Total Column

Abstract. The knowledge of the total column water vapour (TCWV) global distribution is fundamental for climate analysis and weather monitoring. In this work, we present the retrieval algorithm used to derive the operational TCWV from the GOME-2 sensors and perform an extensive inter-comparison and validation in order to estimate their absolute accuracy and long-term stability. We use the recently reprocessed data sets retrieved by the GOME-2 instruments aboard EUMETSAT's MetOp-A and MetOp-B satellites and generated by DLR in the framework of the O3M-SAF using the GOME Data Processor (GDP) version 4.7. The retrieval algorithm is based on a classical Differential Optical Absorption Spectroscopy (DOAS) method and combines H2O/O2 retrieval for the computation of the trace gas vertical column density. We introduce a further enhancement in the quality of the H2O column by optimizing the cloud screening and developing an empirical correction in order to eliminate the instrument scan angle dependencies. We evaluate the overall consistency between about 8 months measurements from the newer GOME-2 instrument on the MetOp-B platform with the GOME-2/MetOp-A data in the overlap period. Furthermore, we compare GOME-2 results with independent TCWV data from ECMWF and with SSMIS satellite measurements during the full period January 2007–August 2013 and we perform a validation against the combined SSM/I + MERIS satellite data set developed in the framework of the ESA DUE GlobVapour project. We find global mean biases as small as ± 0.03 g cm−2 between GOME-2A and all other data sets. The combined SSM/I-MERIS sample is typically drier than the GOME-2 retrievals (−0.005 g cm−2), while on average GOME-2 data overestimate the SSMIS measurements by only 0.028 g cm−2. However, the size of some of these biases are seasonally dependent. Monthly average differences can be as large as 0.1 g cm−2, based on the analysis against SSMIS measurements, but are not as evident in the validation with the ECMWF and the SSM/I + MERIS data. Studying two exemplary months, we estimate regional differences and identify a very good agreement between GOME-2 total columns and all three independent data sets, especially for land areas, although some discrepancies over ocean and over land areas with high humidity and a relatively large surface albedo are also present.

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

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