Leveraging meteorological radars to investigate the influence of atmospheric dynamics on snowfall microphysics

Precipitation is the result of a chain of meteorological processes ranging from the large- to the micro-scale. While the transport of moisture and lifting mechanisms leading to cloud formation are mostly governed by dynamical processes, the formation and growth of hydrometeors are ultimately determined by microphysical processes. A proper understanding of the complex interactions between atmospheric dynamics and microphysics is of paramount importance to accurately forecast precipitation. In particular, snowfall microphysics is greatly influenced by dynamical processes, such as turbulence and updraughts. Yet, the impact of atmospheric dynamics on snowfall microphysics remains poorly understood.In this thesis, meteorological radars and atmospheric models are combined to investigate how dynamical processes can influence snowfall microphysics. We exploit the synergies between measurements collected with an X-band polarimetric radar (named MXPol), a W-band Doppler radar, and a multi-angle snowflake camera (MASC). Hydrometeor classifications are used to identify the key microphysical processes at play. The objectives of this thesis are twofold: (i) collect data on clouds and precipitation during two field campaigns in South Korea and Antarctica, and (ii) leverage this data to investigate how dynamical processes influenced the microphysics of two snowfall events. First, a 4-month dataset of ground-based radar and in situ measurements collected in South Korea is presented. The dataset includes 9 precipitation events with a total accumulation of 195 mm of equivalent liquid precipitation. Second, measurements of clouds and precipitation during a 3-month campaign at Davis station, Antarctica are introduced. Altogether, both datasets collected during this thesis represent an opportunity to study snowfall microphysics thanks to the complementarity of Doppler radar data and snowflake photographs in two regions where such measurements were not available before.The second part of this thesis is devoted to case studies of two snowfall events. First, a case study of an intense precipitation event in South Korea reveals that a warm conveyor belt associated with a low pressure system provided ideal conditions for riming and aggregation through the generation of turbulence and SLW in the large-scale ascent. These processes led to enhanced precipitation rates of up to 12 mm/h and a total accumulation of 57 mm in 21 h. The second case study shows that orographic gravity waves (OGWs) determined the evolution and distribution of snowfall during an atmospheric river event over Davis in the Vestfold Hills, Antarctica. Despite the intense moisture advection by the atmospheric river, little precipitation was observed at Davis due to intense snowfall sublimation by foehn winds associated with the OGWs. We propose that this mechanism could contribute to the extremely dry climate of the Vestfold Hills, one of the rare ice-free regions of Antarctica.Altogether, this thesis offers two unique datasets which are available to the scientific community and can be used for future studies on snowfall microphysics. It also contributes to a better understanding of how atmospheric dynamics can influence snowfall microphysics. In particular, it illustrates how processes occurring at different spatial scales can determine the dominating snowfall microphysical processes.

Identification of snowfall microphysical processes from Eulerian vertical gradients of polarimetric radar variables

Alexis Berne, Josué Etienne Gehring, Noémie Planat, Etienne Gabriel Henri Vignon

Polarimetric radar systems are commonly used to study the microphysics of precipitation. While they offer continuous measurements with a large spatial coverage, retrieving information about the microphysical processes that govern the evolution of snowfall from the polarimetric signal is challenging. The present study develops a new method, called process identification based on vertical gradient signs (PIVSs), to spatially identify the occurrence of the main microphysical processes (aggregation and riming, crystal growth by vapor deposition and sublimation) in snowfall from dual-polarization Doppler radar scans. We first derive an analytical framework to assess in which meteorological conditions the local vertical gradients of radar variables reliably inform about microphysical processes. In such conditions, we then identify regions dominated by (i) vapor deposition, (ii) aggregation and riming and (iii) snowflake sublimation and possibly snowflake breakup, based on the sign of the local vertical gradients of the reflectivity Z(H) and the differential reflectivity Z(DR). The method is then applied to data from two frontal snowfall events, namely one in coastal Adelie Land, Antarctica, and one in the Taebaek Mountains in South Korea. The validity of the method is assessed by comparing its outcome with snowflake observations, using a multi-angle snowflake camera, and with the output of a hydrometeor classification, based on polarimetric radar signal. The application of the method further makes it possible to better characterize and understand how snowfall forms, grows and decays in two different geographical and meteorological contexts. In particular, we are able to automatically derive and discuss the altitude and thickness of the layers where each process prevails for both case studies. We infer some microphysical characteristics in terms of radar variables from statistical analysis of the method output (e.g., Z(H) and Z(DR) distribution for each process). We, finally, highlight the potential for extensive application to cold precipitation events in different meteorological contexts.

COPERNICUS GESELLSCHAFT MBH2021

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

Considering snow depositions and transport mechanisms in meteorological analysis of predicting snow loads on buildings

Michael Lehning, Borui Zhang, Qingwen Zhang

The meteorological conditions determine snow evolutions on and around buildings. Those climatic impacts have been independently considered in the current design codes of buildings, namely the wind pressure and the snow load. But snow load estimates are only based on precipitation assessments without considering the joint effect of wind and snow precipitation which lead to wind dominated deposition and snow transport. Thus, the statisticsbased data provided by those building codes seem practically inadequate or at least incomplete. Using the data provided by upper-air and ground meteorological stations in northern China, including snowfall, wind velocity, and temperature, as indicators, we propose a series of statistical relationships covering aspects of snow precipitation and snow drifting. For those meteorological indicators that can affect building snow evolutions, more detailed analyses on their spatial and temporal changes have been achieved. The regionalization of snow precipitation features in northern China has also been proposed based on the clustering analysis on meteorological joint effects. The result shows that the snowfall in most northern regions of China is generally not heavy and in the form of a uniform deposition. The wind dominated depositions around buildings are more likely to appear in the regions around the Bohai Sea.