Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system are applied to provide complete grids of PWV with high quality. Our goal is to correctly infer PWV at spatially continuous, highly resolved grids from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric PWV produced by combining observations from the Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and a millimeter accuracy; however, the data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a still limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution show better qualities than those inferred from single data sets. In addition, by using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging. © 2015 Author(s)

High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation

The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases and energy. Nonlinear feedback and scale dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or largely bound to traditional disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local area weather prediction. This study examines the ability of the hydrologically enhanced version of the Weather Research and Forecasting Model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assesses the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identical calibrated parameter settings for the land surface model (Noah-MP). The simulations are evaluated based on extensive observations at the preAlpine Terrestrial Environmental Observatory (TERENO-preAlpine) for the Ammer (600 km2) and Rott (55 km2) river catchments in southern Germany, covering a five month period (Jun–Oct 2016). The sensitivity of 7 land surface parameters is tested using the Latin-Hypercube One-factor-At-a-Time (LH-OAT) method and 6 sensitive parameters are subsequently optimized for 6 different subcatchments, using the Model-Independent Parameter Estimation and Uncertainty Analysis software (PEST). The calibration of the offline WRF-Hydro gives Nash-Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of classic WRF and fully coupled WRF-Hydro, both using the calibrated parameters from the offline model, shows nominal alterations for radiation and precipitation but considerable changes for moisture- and heat fluxes. By comparison with TERENO-preAlpine observations, the fully coupled model slightly outperforms the classic WRF with respect to evapotranspiration, sensible and ground heat flux, near surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation whereas soil moisture and precipitation change randomly

Shorter Double-Authentication Preventing Signatures for Small Address Spaces

A recent paper by Derler, Ramacher, and Slamanig (IEEE EuroS&P 2018) constructs double-authentication preventing signatures ( DAP signatures , a specific self-enforcement enabled variant of signatures where messages consist of an address and a payload) that have---if the supported address space is not too large---keys and signatures that are considerably more compact than those of prior work. We embark on their approach to restrict attention to small address spaces and construct novel DAP schemes that beat their signature size by a factor of five and reduce the signing key size from linear to constant (the verification key size remains almost the same). We construct our DAP signatures generically from identification protocols, using a transform similar to but crucially different from that of Fiat and Shamir. We use random oracles. We don\u27t use pairings

Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system is applied to provide complete, accurate grids of PWV. Our goal is to infer spatially continuous, precise grids of PWV from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric water vapor produced by combining observations from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and an accuracy of submillimeter; however, data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a yet limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution are more accurate than those inferred from single data sets. In addition, using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging

Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system are applied to provide complete grids of PWV with high quality. Our goal is to correctly infer PWV at spatially continuous, highly resolved grids from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric PWV produced by combining observations from the Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and a millimeter accuracy; however, the data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a still limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution show better qualities than those inferred from single data sets. In addition, by using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging

Process‐based atmosphere-hydrology-malaria modeling: performance for spatio‐temporal malaria transmission dynamics in Sub‐Saharan Africa

With the goal of eradication by 2030, Malaria poses a significant health threat, profoundly influenced by meteorological and hydrological conditions. In support of malaria vector control efforts, we present a high-resolution, coupled physically-based modeling approach integrating WRF-Hydro and VECTRI. This model approach accurately captures topographic details at the scale of larvae habitats in the Nouna Health and Demographic Surveillance Systems in Sub-Saharan Africa. Our study demonstrates the proficiency of the high-resolution hydrometeorological model, WRF-Hydro, in replicating observed climate characteristics. Comparisons with in-situ local weather data reveal root mean square errors between 0.6 and 0.87 mm/day for rainfall and correlations ranging from 0.79 to 0.87 for temperatures. Additionally, WRF-Hydro's surface hydrology reproduces the seasonal and intraseasonal variability of the ponded water fraction with 96% accuracy, validated against Sentinel-1 data at a 100-m resolution. The VECTRI model demonstrates sensitivity to surface hydrology representation, particularly when comparing conceptual and detailed physical process models, for variables such as larvae density, mosquito abundance, and EIR. The model's ability to replicate the seasonality of malaria transmission aligns well with available cohort malaria data suggesting its potential for predicting the impacts of climate change on mosquito abundance and transmission intensity in endemic tropical and subtropical zones. This integrated approach opens avenues for enhanced understanding and proactive management of malaria