Effects of Incorporating a Brightband Model in a Downward-Looking Radar Rainfall Retrieval Algorithm

West Africa&#8217;s economy is mainly sustained on agriculture and over 70% of crops are rain-fed. Economic growth and food security in this region is therefore highly dependent on the knowledge of rainfall patterns. According to the IPCC, the Global South will seriously suffer from climate change. As traditional rainfall patterns shift, accurate rainfall information becomes crucial for farmers to optimize food production.The scarce rain gauge distribution and data transmission challenges make rainfall analysis difficult in these regions. Satellites could offer a solution to this problem, but present satellite products do not account for local characteristics and perform poorly in West Africa.A rainfall retrieval algorithm, developed within the Schools and Satellites (SaS) project, could overcome the lack of ground data and good rainfall satellite products through earth observation and advanced machine learning. However, to validate such an algorithm requires a high amount of rainfall data from ground stations. Since rain gauges are scarce in West Africa, a (temporary) high density observation network is necessary to strengthen the training and validation dataset provided by TAHMO and GMet ground measurements. SaS therefore engages with schools in Northern Ghana to build a Citizen Observatory.&#160;SaS is being funded by the European Space Agency as one of the pilot projects of CSEOL (Citizen Science and Earth Observation Lab). It is being developed in a cooperation between TU Delft, PULSAQUA, TAHMO Ghana, Smartphones4Water (S4W) and GMet. The Proof-of-Concept Algorithm will be fed with data collected in the Citizen Observatory during the rainy season of 2020.This Citizen Observatory will be built around the already existing infrastructure of a classroom where Climate Change is amongst the topics in the Ghanaian teaching curriculum. We aim to provide a Climate Change educational module that can be used directly by the teachers. The educational module incorporates the building of their own low-cost rain gauge to be used for manual rainfall data collection. This rainfall collection method has already been highly tested by S4W in Nepal. Students will design their own research around the daily rainfall measurements, which they will submit via a web application called Open Data Kit (ODK). The data is being validated by including a picture of the rainfall measurement that is checked with the number passed on by the citizen scientist.The Citizen Observatory will be placed under the existing TAHMO and S4W infrastructures to respectively continue the interaction with schools and to continue data collection, -validation and -visualization. If the algorithm proofs to indeed perform better than current satellite products for the pilot area in Northern Ghana, the Citizen Observatory could in the future help to validate and improve the product for the whole of West-Africa.To enable the use of this Citizen Observatory for management of water resources and in this case more and better rainfall data, much effort is needed. We will demonstrate which measures we have taken to ensure that the Citizen Observatory performs with enough quality, and how (if done well) it has the potential to increase the impact of this study.

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Sensitivity of power functions to aggregation: Bias and uncertainty in radar rainfall retrieval

Water Resources Research ◽

10.1002/2013wr015109 ◽

2014 ◽

Vol 50 (10) ◽

pp. 8050-8065 ◽

Cited By ~ 6

Author(s):

M. G. Sassi ◽

H. Leijnse ◽

R. Uijlenhoet

Keyword(s):

Aggregation Bias ◽

Power Functions ◽

Radar Rainfall ◽

Rainfall Retrieval

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Impacts of Raindrop Fall Velocity and Axis Ratio Errors on Dual-Polarization Radar Rainfall Estimation

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0201.1 ◽

2014 ◽

Vol 15 (5) ◽

pp. 1849-1861 ◽

Cited By ~ 10

Author(s):

Bin Pei ◽

Firat Y. Testik ◽

Mekonnen Gebremichael

Keyword(s):

Rain Rate ◽

Estimation Error ◽

Retrieval Algorithm ◽

Dual Polarization ◽

Fall Velocity ◽

Estimation Errors ◽

Radar Rainfall ◽

Axis Ratio ◽

Rate Estimation ◽

Predicted Values

Abstract Motivated by the field observations of fall velocity and axis ratio deviations from predicted terminal velocity and equilibrium axis ratio values, the combined effects of raindrop fall velocity and axis ratio deviations on dual-polarization radar rainfall estimations were investigated. A radar rainfall retrieval algorithm [Colorado State University–Hydrometeor Identification Rainfall Optimization (CSU-HIDRO)] served as the test bed. Subsequent investigations determined that the available field measurements, which were very limited in scope, of the fall velocity and axis ratio deviations indicated rain-rate estimation errors of approximately 20%. Based on these findings, a sensitivity study was then performed using uncorrelated fall velocity and axis ratio deviations around the predicted values. Significant rain-rate estimation errors were observed for the realistic combinations of fall velocity and axis ratio deviations. It was shown that the maximum rain-rate estimation error can reach up to approximately 200% for combinations of fall velocity and axis ratio deviations (5000 drop size distribution samples were simulated for each combination) between −10% and +10% of the predicted values for each. The maximum standard deviation of errors was as great as 75% for the same combinations of fall velocity and axis ratio deviations. The authors found that use of dual-polarization radars to accurately estimate rainfall, during natural rain events, also requires a reanalysis of the parameterizations for raindrop fall velocity and axis ratio. These parameterizations should consider both the coupling between these two parameters and factors that may introduce any possible deviations of the predicted values of these parameters.

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A measurement campaign to assess sources of error in microwave link rainfall estimation

Atmospheric Measurement Techniques ◽

10.5194/amt-11-4645-2018 ◽

2018 ◽

Vol 11 (8) ◽

pp. 4645-4669 ◽

Cited By ~ 13

Author(s):

Thomas C. van Leth ◽

Aart Overeem ◽

Hidde Leijnse ◽

Remko Uijlenhoet

Keyword(s):

Communication Networks ◽

Global Analysis ◽

Time Lapse ◽

Rain Gauge ◽

Operating Conditions ◽

Retrieval Algorithm ◽

Measurement Campaign ◽

Temporal Sampling ◽

Microwave Link ◽

Rainfall Retrieval

Abstract. We present a measurement campaign to address several error sources associated with rainfall estimates from microwave links in cellular communication networks. The core of the experiment is provided by three co-located microwave links installed between two major buildings on opposite sides of the small town of Wageningen, approximately 2 km apart: a 38 GHz formerly commercial microwave link, as well as 26 and 38 GHz (dual-polarization) research microwave links. Transmitting and receiving antennas have been attached to masts installed on the roofs of the two buildings, about 30 m above the ground. This setup was complemented with an infrared large-aperture scintillometer, installed over the same path, as well as five laser disdrometers positioned at several locations along the path and an automated rain gauge. Temporal sampling of the received signals was performed at a rate of 20 Hz. The setup was monitored by time-lapse cameras to assess the state of the antennas as well as the atmosphere. The experiment was active between August 2014 and December 2015. Data from an existing automated weather station situated just outside Wageningen was further used to compare and to interpret the findings. In addition to presenting the experiment, we also conduct a preliminary global analysis and show several cases highlighting the different phenomena affecting received signal levels: rainfall, solid precipitation, temperature, fog, antenna wetting due to rain or dew, and clutter. We also briefly explore cases where several phenomena play a role. A rainfall intensity (R) – specific attenuation (k) relationship was derived from the disdrometer data. We find that a basic rainfall retrieval algorithm without corrections already provides a reasonable correlation to rainfall as measured by the disdrometers. However, there are strong systematic overestimations (factors of 1.2–2.1) which cannot be attributed to the R–k relationship. We observe attenuations in the order of 3 dB due to antenna wetting under fog or dew conditions. We also observe fluctuations of a similar magnitude related to changes in temperature. The response of different makes of microwave antennas to many of these phenomena is significantly different even under the exact same operating conditions and configurations.

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Stochastic simulation experiment to assess radar rainfall retrieval uncertainties associated with attenuation and its correction

Hydrology and Earth System Sciences ◽

10.5194/hess-12-587-2008 ◽

2008 ◽

Vol 12 (2) ◽

pp. 587-601 ◽

Cited By ~ 25

Author(s):

R. Uijlenhoet ◽

A. Berne

Keyword(s):

Simulation Experiment ◽

Temporal Distribution ◽

Single Frequency ◽

Forward Algorithm ◽

Radar Rainfall ◽

Weather Radars ◽

Wide Range ◽

Order Of Magnitude ◽

Raindrop Size ◽

Rainfall Retrieval

Abstract. As rainfall constitutes the main source of water for the terrestrial hydrological processes, accurate and reliable measurement and prediction of its spatial and temporal distribution over a wide range of scales is an important goal for hydrology. We investigate the potential of ground-based weather radar to provide such measurements through a theoretical analysis of some of the associated observation uncertainties. A stochastic model of range profiles of raindrop size distributions is employed in a Monte Carlo simulation experiment to investigate the rainfall retrieval uncertainties associated with weather radars operating at X-, C-, and S-band. We focus in particular on the errors and uncertainties associated with rain-induced signal attenuation and its correction for incoherent, non-polarimetric, single-frequency, operational weather radars. The performance of two attenuation correction schemes, the (forward) Hitschfeld-Bordan algorithm and the (backward) Marzoug-Amayenc algorithm, is analyzed for both moderate (assuming a 50 km path length) and intense Mediterranean rainfall (for a 30 km path). A comparison shows that the backward correction algorithm is more stable and accurate than the forward algorithm (with a bias in the order of a few percent for the former, compared to tens of percent for the latter), provided reliable estimates of the total path-integrated attenuation are available. Moreover, the bias and root mean square error associated with each algorithm are quantified as a function of path-averaged rain rate and distance from the radar in order to provide a plausible order of magnitude for the uncertainty in radar-retrieved rain rates for hydrological applications.

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Estimation of Finescale Rainfall Fields Using Broadcast TV Satellite Links and a 4DVAR Assimilation Method

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-14-00125.1 ◽

2015 ◽

Vol 32 (10) ◽

pp. 1709-1728 ◽

Cited By ~ 20

Author(s):

François Mercier ◽

Laurent Barthès ◽

Cécile Mallet

Keyword(s):

Hydrological Cycle ◽

Real Data ◽

Rain Attenuation ◽

Rain Gauge ◽

Propagation Model ◽

Retrieval Algorithm ◽

High Temporal Resolution ◽

Radar Rainfall ◽

Rain Gauge Data ◽

Gauge Data

AbstractThis study proposes a method based on the use of a set of commercial satellite-to-Earth microwave links to rebuild finescale rainfall fields. Such microwave links exist all over the world and can be used to estimate the integrated rain attenuation over the links’ first 5–7 km with a very high temporal resolution (10 s in the present case). The retrieval algorithm makes use of a four-dimensional variational data assimilation (4DVAR) method involving a numerical advection scheme. The advection velocity is recovered from the observations or from radar rainfall fields at successive time steps.This technique has been successively applied to simulated 2D rain maps and to real data recorded in the autumn of 2013 during the Hydrological Cycle in the Mediterranean Experiment (HyMeX), with one sensor receiving microwave signals from four different satellites. The performance of this system is assessed and is compared to an operational Météo-France radar and a network of 10 rain gauges. Because of the limitations of the propagation model, this study is limited to the events with strong advective characteristics (four out of eight recorded events). For these events (only), the method produces rainfall fields that are highly correlated with the radar maps at spatial resolutions greater than . The point-scale results are also satisfactory for temporal resolutions greater than 10 min (mean correlation with rain gauge data equal to approximately 0.8, similar to the correlation between radar and rain gauge data).This method can also be adapted to the fusion of a rain gauge with microwave link measurements and, through the use of several sensors, it has the potential of being applied to larger areas.

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