Combining commercial microwave link and rain gauge observations to estimate countrywide precipitation: a stochastic reconstruction and pattern analysis approach

Accurate spatiotemporal precipitation quantification is a crucial prerequisite for hydrological analyses. The optimal reconstruction of the spatial distribution, that is, the rainfall patterns, is particularly challenging. In this study, we reconstructed spatial rainfall on a countrywide scale for Germany by combining commercial microwave link and rain gauge observations for a better representation of the variability and spatial structure of rainfall. We further developed and applied the Random-Mixing-Whittaker-Shannon method, enabling the stochastic reconstruction of ensembles of spatial fields via linear combinations of unconditional random fields. The pattern of rainfall objects is evaluated by three performance characteristics, that is, ensemble Structure-, Amplitude-, and Location-error. Precipitation estimates obtained are in good agreement with the gauge-adjusted weather radar product RADOLAN-RW of the German Weather Service (DWD) which was used as a reference. Compared to reconstructions by Ordinary Kriging, Random Mixing showed clear advantages in the pattern representation via a five times smaller median structure error

Stochastic Reconstruction and Interpolation of Precipitation Fields Using Combined Information of Commercial Microwave Links and Rain Gauges

For the reconstruction and interpolation of precipitation fields, we present the application of a stochastic approach called Random Mixing. Generated fields are based on a data set consisting of rain gauge observations and path-averaged rain rates estimated using Commercial Microwave Link (CML) derived information. Precipitation fields are received as linear combination of unconditional spatial random fields, where the spatial dependence structure is described by copulas. The weights of the linear combination are optimized such that the observations and the spatial structure of the precipitation observations are reproduced. The innovation of the approach is that this strategy enables the simulation of ensembles of precipitation fields of any size. Each ensemble member is in concordance with the observed path-averaged CML derived rain rates and additionally reflects the observed rainfall variability along the CML paths. The ensemble spread allows additionally an estimation of the uncertainty of the reconstructed precipitation fields. The method is demonstrated both for a synthetic data set and a real-world data set in South Germany. While the synthetic example allows an evaluation against a known reference, the second example demonstrates the applicability for real-world observations. Generated precipitation fields of both examples reproduce the spatial precipitation pattern in good quality. A performance evaluation of Random Mixing compared to Ordinary Kriging demonstrates an improvement of the reconstruction of the observed spatial variability. Random Mixing is concluded to be a beneficial new approach for the provision of precipitation fields and ensembles of them, in particular when different measurement types are combined

Combining Commercial Microwave Link and Rain Gauge Observations to Estimate Countrywide Precipitation: A Stochastic Reconstruction and Pattern Analysis Approach

Accurate spatiotemporal precipitation quantification is a crucial prerequisite for hydrological analyses. The optimal reconstruction of the spatial distribution, that is, the rainfall patterns, is particularly challenging. In this study, we reconstructed spatial rainfall on a countrywide scale for Germany by combining commercial microwave link and rain gauge observations for a better representation of the variability and spatial structure of rainfall. We further developed and applied the Random-Mixing-Whittaker-Shannon method, enabling the stochastic reconstruction of ensembles of spatial fields via linear combinations of unconditional random fields. The pattern of rainfall objects is evaluated by three performance characteristics, that is, ensemble Structure-, Amplitude-, and Location-error. Precipitation estimates obtained are in good agreement with the gauge-adjusted weather radar product RADOLAN-RW of the German Weather Service (DWD) which was used as a reference. Compared to reconstructions by Ordinary Kriging, Random Mixing showed clear advantages in the pattern representation via a five times smaller median structure error

Towards Space Deployment of the NDSA Concept for Tropospheric Water Vapour Measurements

A novel measurement concept specifically tuned to monitoring tropospheric water vapour's vertical distribution has been demonstrated on a theoretical basis and is currently under development for space deployment. The NDSA (Normalised Differential Spectral Attenuation) technique derives the integrated water vapour (IWV) along the radio link between a transmitter and a receiver carried by two LEO satellites, using the linear correlation between the IWV and a parameter called spectral sensitivity. This is the normalised incremental ratio of the spectral attenuation at two frequencies in the Ku and K bands, with the slope of the water vapour absorption line at 22.235 GHz. Vertical profiles of WV can be retrieved by inverting a set of IWV measurements acquired in limb geometry at different tangent altitudes. This paper provides a comprehensive insight into the NDSA approach for sounding lower tropospheric WV, from the theoretical investigations in previous ESA studies, to the first experimental developments and testing, and to the latest advancements achieved with the SATCROSS project of the Italian Space Agency. The focus is on the new results from SATCROSS activities; primarily, on the upgrading of the instrument prototype, with improved performance in terms of its power stability and the time resolution of the measurements. Special emphasis is also placed on discussing tomographic inversion methods capable of retrieving tropospheric WV content from IWV measurements, i.e., the least squares and the external reconstruction approaches, showing results with different spatial features when applied to a given atmospheric scenario. The ultimate goal of deploying the NDSA measurement technique from space is thoroughly examined and conclusions are drawn after presenting the results of an Observing System Simulation Experiment conducted to assess the impact of NDSA data assimilation on environmental model simulations

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

Transboundary Rainfall Estimation Using Commercial Microwave Links

Unlike actual rainfall, the spatial extent of rainfall maps is often determined by administrative and political boundaries. Similarly, data from commercial microwave links (CMLs) is usually acquired on a national basis and exchange among countries is limited. Up to now, this has prohibited the generation of transboundary CML-based rainfall maps despite the great extension of networks across the world. We present CML based transboundary rainfall maps for the first time, using independent CML data sets from Germany and the Czech Republic. We show that straightforward algorithms used for quality control strongly reduce anomalies in the results. We find that, after quality control, CML-based rainfall maps can be generated via joint and consistent processing, and that these maps allow to seamlessly visualize rainfall events traversing the German-Czech border. This demonstrates that quality control represents a crucial step for large-scale (e.g., continental) CML-based rainfall estimation

Transboundary rainfall estimation using commercial microwave links

Unlike actual rainfall, the spatial extent of rainfall maps is often determined by administrative and political boundaries. Similarly, data from commercial microwave links (CMLs) is usually acquired on a national basis and exchange among countries is limited. Up to now, this has prohibited the generation of transboundary CML-based rainfall maps despite the great extension of networks across the world. We present CML based transboundary rainfall maps for the first time, using independent CML data sets from Germany and the Czech Republic. We show that straightforward algorithms used for quality control strongly reduce anomalies in the results. We find that, after quality control, CML-based rainfall maps can be generated via joint and consistent processing, and that these maps allow to seamlessly visualize rainfall events traversing the German-Czech border. This demonstrates that quality control represents a crucial step for large-scale (e.g., continental) CML-based rainfall estimation

Rainfall over the Netherlands & beyond: a remote sensing perspective

Earthlings like to measure everything (especially now that we are undergoing the era of big-data revolution) maybe because it is such a nice hobby... although a more serious school of thought believes that when measuring our environment we get to understand physics and ourselves. This thesis explores the uncertainties in rainfall measurements from state-of-the-art technologies like commercial microwave links (CML) and meteorological satellites. Rainfall has been measured by rain gauges since quite some time ago; and by weather radars since the end of WWII. Here we evaluate the performance of gridded-rainfall products for the land surface of the Netherlands. These gridded-rainfall products are CML-rainfall maps produced by the Royal Netherlands Meteorology Institute (KNMI), and the IMERG product developed by Global Precipitation Measurement mission (GPM). Overall, this thesis shows that CML-rainfall products are very reliable sources with regards to rainfall estimates for the land surface of the Netherlands... even better than the satellite products for rainfall estimation. We are also confident in the promising potential these technologies hold for places around the world where conventional technologies like gauges or radars are not scarce or not affordable. </p

Sensing the dynamics of severe weather using 4D GPS tomography in the Australian region

The dynamics of water vapour (WV) have a strong influence on weather and climate due to the large energy transfers in the hydrological processes. This particularly correlates to WV dynamics during the formation and lifecycle of severe mesoscale convective storm and precipitation systems. Contrary to its importance, WV remains poorly understood and inadequately measured both spatially and temporally, especially in Australia and the southern hemisphere where meteorological data are sparse. Ground-based and space-borne GPS (global positioning system) meteorology are currently regarded as leading atmospheric remote sensing instruments for numerical weather prediction (NWP) and climatology due to their high spatio-temporal resolutions, multiple observing platforms and continuous operability. The GPS signals are delayed and bent due to the refractive index of the ionosphere and troposphere. This tropospheric path delay can be separated into dry and wet integral components, with the latter proportional, using a scale factor, to the integrated precipitable water vapour (PWV) in the vertical column above the GPS stations. These wet delay measurements can also be combined using a network of GPS stations to resolve the spatial distribution of WV. This method is called GPS tomography, which is a promising and developing method of reconstructing dynamically changing four dimensional (4D) wet refractivity fields. This takes advantage of the high density and homogeneity of ground-based GPS Continuously Operating Reference Station (CORS) networks to provide accurately resolved WV profiles in space and time. A distinct trend between the 4D reconstructed wet refractivity fields using GPS tomography and the formation and lifecycle of severe storm and precipitation systems was found. Sharp gradients are evident up the vertical layers The dynamics of water vapour (WV) have a strong influence on weather and climate due to the large energy transfers in the hydrological processes. This particularly correlates to WV dynamics during the formation and lifecycle of severe mesoscale convective storm and precipitation systems. Contrary to its importance, WV remains poorly understood and inadequately measured both spatially and temporally, especially in Australia and the southern hemisphere where meteorological data are sparse. Ground-based and space-borne GPS (global positioning system) meteorology are currently regarded as leading atmospheric remote sensing instruments for numerical weather prediction (NWP) and climatology due to their high spatio-temporal resolutions, multiple observing platforms and continuous operability. The GPS signals are delayed and bent due to the refractive index of the ionosphere and troposphere. This tropospheric path delay can be separated into dry and wet integral components, with the latter proportional, using a scale factor, to the integrated precipitable water vapour (PWV) in the vertical column above the GPS stations. These wet delay measurements can also be combined using a network of GPS stations to resolve the spatial distribution of WV. This method is called GPS tomography, which is a promising and developing method of reconstructing dynamically changing four dimensional (4D) wet refractivity fields. This takes advantage of the high density and homogeneity of ground-based GPS Continuously Operating Reference Station (CORS) networks to provide accurately resolved WV profiles in space and time. A distinct trend between the 4D reconstructed wet refractivity fields using GPS tomography and the formation and lifecycle of severe storm and precipitation systems was found. Sharp gradients are evident up the vertical layers providing the wet refractivity trend of convection, with high gradient falls through the vertical layers after the storm system passed. Radiosonde is used as a reference to validate the GPS tomographic model with final accuracies of the March 2010 and January 2011 case studies presenting 8.58 and 9.36 ppm RMS errors, respectively. A wet refractivity index adopted for the GPS tomographic wet refractivity profiles showed an excessive increase above the planetary boundary layer as a response to the formation of a supercell thunderstorm. Finally, horizontal and vertical 2D cross sections, investigating the evolution of the March 2010 severe weather event concludes a high correlation between the highly dynamic spatial and temporal changes of wet refractivity, modelled using 4D GPS tomography with precipitation intensities measured using weather radars images. These gradient solutions from GPS tomography are able to identify the spatial and temporal structure of the mesoscale convective and stratiform processes during severe weather. Final investigations analyse the influence of additional observational methods introduced into the observation model of the GPS tomographic processing. This analysis is conducted during the formation and lifecycle of severe weather of the January 2011 case study. A statistical analysis compares additional observational methods including: radiosonde, synoptic weather station networks and GPS radio occultation and then the influence of all observation methods combined. The results are compared against radiosonde-derived wet refractivity estimates as the reference data to conclude RMS errors of 9.36, 8.03, 8.14, 8.56 and 7.57 ppm, respectively. These results have shown that the introduction of accurate additional information into the tomographic solution lead to a significant increase in accuracy and more robust results than the original method containing no additional data. These improvements are in the order of 14.29%, 13.09%, 8.57% and 19.15%, respectively. The major objectives of this research are satisfied by developing ground-based GPS meteorological platforms in the Australian region including the introduction of 4D tomographic reconstruction methods to the GPSnet. These developments are in view of assimilation methods for nowcasting and NWP to provide a more robust platform for early detection and prediction of severe weather and precipitation extremes