Optimum estimation of rain microphysical parameters from X-band dual-polarization radar observables

Modern polarimetric weather radars typically provide reflectivity, differential reflectivity, and specific differential phase shift, which are used in algorithms to estimate the parameters of the rain drop size distribution (DSD), the mean drop shape, and rainfall rate. A new method is presented to minimize the parameterization error using the Rayleigh scattering limit relations multiplied with a rational polynomial function of reflectivity-weighted raindrop diameter to approximate the Mie character of scattering. A statistical relation between the shape parameter of the DSD with the median volume diameter of raindrops is derived by exploiting long-term disdrometer observations. On the basis of this relation, new optimal estimators of rain microphysical parameters and rainfall rate are developed for a wide range of rain DSDs and air temperatures using X-band scattering simulations of polarimetric radar observables. Parameterizations of radar specific path attenuation and backscattering phase shift are also developed, which do not depend on this relation. The methodology can, in principle, be applied to other weather radar frequencies. A numerical sensitivity analysis shows that calibration bias and measurement noise in radar measurements are critical factors for the total error in parameters estimation, despite the low parameterization error (less than 5%). However, for the usual errors of radar calibration and measurement noise (of the order of 1 dB, 0.2 dB, and 0.3 deg km(-1) for reflectivity, differential reflectivity, and specific differential propagation phase shift, respectively), the new parameterizations provide a reliable estimation of rain parameters (typically less than 20% error).Modern polarimetric weather radars typically provide reflectivity, differential reflectivity, and specific differential phase shift, which are used in algorithms to estimate the parameters of the rain drop size distribution (DSD), the mean drop shape, and rainfall rate. A new method is presented to minimize the parameterization error using the Rayleigh scattering limit relations multiplied with a rational polynomial function of reflectivityweighted raindrop diameter to approximate the Mie character of scattering. A statistical relation between the shape parameter of the DSD with the median volume diameter of raindrops is derived by exploiting long-term disdrometer observations. On the basis of this relation, new optimal estimators of rain microphysical parameters and rainfall rate are developed for a wide range of rain DSDs and air temperatures using X-band scattering simulations of polarimetric radar observables. Parameterizations of radar specific path attenuation and backscattering phase shift are also developed, which do not depend on this relation. The methodology can, in principle, be applied to other weather radar frequencies. A numerical sensitivity analysis shows that calibration bias and measurement noise in radar measurements are critical factors for the total error in parameters estimation, despite the low parameterization error (less than 5%). However, for the usual errors of radar calibration and measurement noise (of the order of 1 dB, 0.2 dB, and 0.3 deg km−1 for reflectivity, differential reflectivity, and specific differential propagation phase shift, respectively), the new parameterizations provide a reliable estimation of rain parameters (typically less than 20% error)

Investigating Small-Scale Microphysical and Dynamical Mechanisms Within a Winter Orographic Snowfall Event and a Spring Squall Line Interacting with Mountains

The first part of this dissertation investigates natural small-scale microphysical and dynamical mechanisms identified in a winter orographic snowstorm over the Sierra Madre mountain range of Wyoming during the AgI Seeding Clouds Impact Investigation (ASCII). A turbulent shear layer was observed in a cold post-frontal environment that was created by a mid-level cross-barrier jet riding over a decoupled Arctic air mass. Similar turbulent shear layers have been observed over blocked low-level air masses along coastal maritime mountain ranges, but little research has focused on inland continental ranges. The multi-instrument analysis suggests 1) shear-induced turbulent overturning cells do exist over cold continental mountain ranges like the Sierra Madre, 2) the presence of cross-barrier jets favor these turbulent shear zones, 3) this turbulence is a key mechanism in enhancing snow growth, and 4) snow growth enhanced by turbulence primarily occurs through deposition and aggregation in these cold (< -15°C) post-frontal continental environments. The second part of this dissertation utilizes a high-resolution observational network from the Integrated Precipitation and Hydrology Experiment (IPHEx) to document the orographic modification of a prefrontal squall line that passed over the southern Appalachian Mountains. Little previous research exists documenting the interaction of squall lines with mountainous terrain, especially observationally, so this study is one of the first. The squall line studied was embedded within an Atmospheric River (AR), where southerly low-level moisture transport was impeded by the southern Appalachian Mountains, favoring rapid fallout of precipitation on its southeastern slopes. A growing research interest exists in the role ARs play in extreme precipitation events over the eastern US, and this study highlights the importance of small-scale terrain and convective features within AR environments in generating heavy rainfall. The third part of this dissertation describes i) my first-of-its-kind NOAA G-IV tail Doppler radar analysis over the Pacific Ocean aimed at documenting cloud and precipitation structures within an offshore AR during the CalWater-2 field project, and ii) my role in collecting ground-breaking radar data during the SNOWIE field project that is being used to document the formation and fallout of snow initiated by man-made airborne glaciogenic cloud seeding

Rainfall monitoring with opportunistic sensors

High-resolution rainfall observations are desirable, especially in urban areas. However, most traditional rain sensors measure rain indirectly at a considerable elevation (weather radar), or only in rural areas (WMO standard rain gauges). Opportunistic sensors are devices that are not intended for large-scale rainfall monitoring, but can be used as such. One example is commercial microwave links: microwave signals are transmitted between antennas for the purpose of telecommunication, and the signal power drop due to rainfall can be used to estimate link path-averaged rain rate. Another technique is crowdsourcing of rainfall measurements by amateur weather stations from online weather platforms. These two techniques provide rainfall information based on the already existing infrastructure. Quality control and rainfall retrieval methods are applied to obtain valuable rainfall information. With validation studies the relative potential for operational rainfall monitoring is demonstrated.</p

Variability of the raindrop size distribution across scales in Mediterranean rainfall:characterisation and stochastic simulation

Measurement of rain is made difficult by the high variability of the precipitation process, down to raindrop scale. Point measurements are generally accurate, but their lack of spatial representativeness is a significant limitation. Weather radars indirectly measure rainfall over large regions, but the microphysical properties of the rain being measured must be known or inferred in order to compute rainfall quantities from radar data. The raindrop size distribution (DSD) statistically describes the microstructure of rain. While the DSD is often assumed to be uniform in space, it is in fact highly variable. The work in this thesis contributes to the understanding of the small-scale variability of the DSD and its effects on the measurement of rainfall. The methods shown were developed using data from a network of disdrometers and radars over a 13 x 7 km2 field site in Ardeche, France. This area experiences heavy Mediterranean rainfall. A technique to improve the accuracy of DSD measurements made by Parsivel disdrometers is proposed. The method uses a 2D-video-disdrometer as a reference instrument. A new geostatistical method for spatial interpolation and stochastic simulation of the experimental DSD is provided. It can estimate or simulate the non-parametric DSD at an unmeasured location, conditional on nearby measurements. Leave-one-out testing showed that estimates were produced with minimal bias. The correction and simulation techniques were used together to investigate the small-scale variability of the DSD in the study region. DSD variability was studied in detail over two typical scales, corresponding to the footprint size of the Global Precipitation Mission (GPM) space-borne weather radar, and a typical size for an operational numerical weather model pixel. It is shown that the assumption that a point measurement of the DSD represents an areal estimation introduces error that increases with areal size and drop size. Satellite and weather model rainfall retrieval algorithms that correspond to these two typical domains were tested, and while it was found that rain intensity and radar reflectivity were well retrieved, other DSD properties were often not representative of the sub-grid process. Double-moment normalisation provides a compact representation of the DSD, under the assumption that the normalised version DSD is invariant. This assumption was tested using instrument network data in France, Switzerland, and the United States. It is shown in this work that for practical purposes, the double-normalised DSD can be assumed invariant through horizontal and vertical displacement. Using this assumption, a new method for retrieval of the DSD from polarimetric radar data is proposed. The new DSD-retrieval technique performs as well or better than an existing method. An application of multifractal analysis to high-resolution snowfall data from the Swiss Alps is presented. Scaling of snowfall was observed in reconstructed vertical columns, at scales from about 35 metres to two metres, with no scaling observed at smaller scales. Weak scaling was observed in time series. The results indicate that at small (sub-metre or sub-minute) scale, snowfall appears homogeneously distributed