Satellite temperature measurements

Satellite temperature measurements are inferences of the temperature of the atmosphere at various altitudes as well as sea and land surface temperatures obtained from radiometric measurements by satellites. These measurements can be used to locate weather fronts, monitor the El Niño-Southern Oscillation, determine the strength of tropical cyclones, study urban heat islands and monitor the global climate. Wildfires, volcanos, and industrial hot spots can also be found via thermal imaging from weather satellites. Weather satellites do not measure temperature directly. They measure radiances in various wavelength bands. Since 1978 microwave sounding units (MSUs) on National Oceanic and Atmospheric Administration polar orbiting satellites have measured the intensity of upwelling microwave radiation from atmospheric oxygen, which is related to the temperature of broad vertical layers of the atmosphere. Measurements of infrared radiation pertaining to sea surface temperature have been colle

Stream temperature modeling using fiber optic distributed temperature measurements in a sub-Saharan watershed, Burkina Faso

Malik Matthey

High spatial and temporal resolution water temperature measurements, using DTS, allow to model and to control stream temperature evolution in the Tambarga’s basin (Burkina Faso). A wireless sensor network recorded solar radiation, air temperature, air humidity, wind speed (among others) on different sites on Tambarga’s basin (B.F.) in 2009. These data are compiled and used as inputs to force the model and to assess simulated river temperatures in the basin. Local parameters as shade coefficient, stream bed heat losses as well as land coverage coefficient are estimated from on field knowledge. Purpose but also need for this study, stream discharge estimation in the Tambarga’s river is paramount. Discharges are formed by subsurface flow, groundwater flow and runoff during rain events. Assuming that all three water discharge components have a different thermal signature leads to imagine that they can be identified with the DTS optical fiber, once they reach the main stream. The inflow emplacements are referred to as springs in this study. Finally, although this study leads to significant results, the last sections propose the installation of some more measuring instruments for controlling and improving the model accuracy.

2010

Improving Alpine Flood Prediction through Hydrological Process Characterization and Uncertainty Analysis

Cara Christine Tobin

Among the many challenges of Alpine flood prediction is describing complex, meteo-hydrological processes in a simplified, robust manner that can be easily integrated into operational forecasting. In this dissertation, improved methods to characterize these processes are developed and integrated into the hydrological modeling component of an operational flood forecasting system used in the Swiss Alps. Detailed studies are conducted to improve hydrological model inputs, processes and outputs. Improvements, detailed in four chapters of this thesis, address the overarching goal of this work – the reduction of flood forecasting uncertainty. The accuracy of flood predictions in Alpine areas is contingent upon adequate interpolation of meteorological forcings, which has significant impacts on discharge volumes and flood peaks. This thesis demonstrates an improvement in the interpolation of temperature and precipitation inputs using a robust variogram which considers anisotropy and using a geostatistical interpolation method to distribute inputs in space and time. Results show that using elevation as the external drift factor better describes orographically-induced precipitation and temperature gradients. Also, the consideration of anisotropy is integral in detailing spatial patterns of precipitation induced by storm advection. Hydrological flood forecasting in mountainous areas also requires accurate partitioning between rain and snowfall to properly estimate the extent of runoff contributing areas. Partitioning is improved in this work by using a new method to integrate Limited Area Model output. Unlike standard hydrological procedures in inferring snowfall limit estimates based on dry, ground temperature measurements, Limited Area Model output considers the vertical, humid, atmospheric structure in its snowfall limit calculations. In effect, this method provides good estimates of runoff contributing areas in the spring as evidenced by validation on discharge measurements and satellite images of snow coverage. Accurately describing snowmelt processes on a sub-daily scale is also of critical importance in Alpine flood forecasting. However, the complex topography of the study region has limited observations available for validation. This thesis presents the development of a new physically-based snowmelt method applicable to regions with limited data. This method uses only daily minimum and maximum temperatures to mimic the effects of radiation. A comparative analysis of snowmelt methods is validated with snow lysimeter data and with a unique, distributed meteorological dataset collected by a wireless weather station network. Results demonstrate that the new method is competitive with more complex snowmelt methods as shown by accurately reproducing diurnal snowmelt cycles. Conveying limits of certainty on flood prediction outputs to users is critical because of epistemic and aleatory errors inherent to environmental modeling. Due to the presence of these errors, the GLUE methodology and multi-criteria performance ideas have been adapted with a fit-for-purpose uncertainty estimation technique in the final part of this thesis. With this method, hydrological model parameters are constrained based on hydrograph behavior, with a particular focus on flood peak response. A key component of the technique is a visualization tool which shows acceptable ensembles of discharge with respect to individual and combined criteria. By integrating the aforementioned input and process improvements into the hydrological model, calibration achieves model outputs that capture observed river discharge. Also, the uncertainty associated with hydrological modeling output error is reduced. Findings of this thesis are applicable to operational flood forecasting in general and have proven utility in improving hydrological model predictions in mountainous regions. Due to the novelty of the developments in terms of new methods or the use of tools and data sources previously unexploited in flood forecasting, further testing of the improvements is recommended. Future research in quantifying the chain of uncertainty produced by combining probabilistic forecast inputs with the hydrological output ensembles is also critical when the improved flood forecasting model becomes fully operational.

EPFL2012

Improving the Turbulence Coupling between High Resolution Numerical Weather Prediction Models and Lagrangian Particle Dispersion Models

Balazs Szintai

For the modelling of the transport and diffusion of atmospheric pollutants during accidental releases, sophisticated emergency response systems are used. These modelling systems usually consist of three main parts. The atmospheric flow conditions can be simulated with a numerical weather prediction (NWP) model. The evolution of the pollutant cloud is described with a dispersion model of variable complexity. The NWP and the dispersion models have to be coupled with a so-called meteorological pre-processor. This means that all the necessary – in most cases turbulence related – variables which are not available from the standard output of the NWP model have to be diagnosed. The main difficulty of the turbulence coupling is that these subgrid scale variables of NWP models are not routinely verified and thus little is known concerning their quality and impact on dispersion processes. The general aim of the present work is to better understand and improve this coupling mechanism. For this purpose all the three main components of the emergency response system of MeteoSwiss are carefully evaluated and possible improvement strategies are suggested. In the first part, the NWP component of the system, namely the COSMO model, is investigated focusing on the model performance in the Planetary Boundary Layer (PBL). Three case studies, representing both unstable and stable situations, are analyzed and the COSMO simulations are validated with turbulence measurements and Large Eddy Simulation (LES) data. It is shown that the COSMO model is able to reproduce the main evolution of the boundary layer in dry convective situations with the operational parameter setting. However, it is found that the COSMO model tends to simulate a too moist and too cold PBL with shallower PBL heights than observed. During stable conditions the operational parameter setting has to be significantly modified (e.g., the minimum diffusion coefficient) to obtain a good model performance. The turbulence scheme of COSMO, which carries a prognostic equation for Turbulent Kinetic Energy (TKE), is studied in detail to understand the shortcomings of the simulations. The turbulent transport term (third order moment) in the TKE equation is found to be significantly underestimated by the COSMO model during unstable situations. This results in inaccurate TKE profiles and hence missing entrainment fluxes at the top of the PBL. A solution to increase the TKE transport in the PBL is proposed in the present work and evaluated during a three-month continuous period. While improving the TKE profile substantially, the modification is demonstrated to not impair other model output characteristics. The second component of the emergency response system, namely the meteorological pre-processor, is also validated on case studies and a continuous period. The main objective of this analysis is to compare the currently operational coupling approach, which is based on the direct usage of the prognostic TKE from the COSMO model, to a classical approach based on similarity theory considerations, thereby using turbulence measurements on the one hand and LES data on the other hand. To be able to use similarity theory approaches for the determination of turbulence characteristics, the PBL height has first to be diagnosed from the NWP model. In the present study, several approaches for the determination of PBL height have been implemented and validated with radio sounding measurements. Based on the verification results and the operational convenience, the method based on the bulk Richardson number method has been chosen for the diagnosis of the PBL height. Validation results of post-diagnosed turbulence characteristics show that during convective situations, the similarity approach tends to overestimate the turbulence intensity, while the approach based on the direct usage of TKE yields more accurate results. For stable conditions the different approaches are closer to each other and both give reasonable predictions. It is found that the main drawback of the direct approach is the isotropic assumption in the horizontal direction. A new hybrid method is proposed which uses similarity considerations for the partitioning of horizontal TKE between along-wind and cross-wind directions. In the last part, pollutant dispersion in complex terrain is studied using a new scaling approach for TKE that is suited for steep and narrow Alpine valleys. This scaling approach is introduced in the interface between COSMO and a Lagrangian particle dispersion model (LPDM), and its results are compared to those of a classical similarity theory approach and to the operational coupling type, which uses the TKE from the COSMO model directly. For the validation of the modelling system, the TRANSALP-89 tracer experiment is used, which was conducted in highly complex terrain in southern Switzerland. The ability of the COSMO model to simulate the valley-wind system is assessed with several meteorological surface stations. The dispersion simulation is evaluated with the measurements from 25 surface samplers. The sensitivity of the modelling system towards the soil moisture, horizontal grid resolution, and boundary layer height determination is investigated. It is shown that if the flow field is correctly reproduced, the new scaling approach improves the tracer concentration simulation compared to the classical coupling methods.

EPFL2010