Identification of Enhanced Rainfall Rates Using the Near-Storm Environment for Radar Precipitation Estimates

Reliable and timely flash flood warnings are critically dependent on the accuracy of real-time rainfall estimates. Precipitation is not only the most vital input for basin-scale accumulation algorithms such as the Flash Flood Monitoring and Prediction (FFMP) program used operationally by the U.S. National Weather Service, but it is the primary forcing for hydrologic models at all scales. Quantitative precipitation estimates (QPE) from radar are widely used for such a purpose due to their high spatial and temporal resolution compared to rain gauges and satellite-based algorithms. However, converting the native radar variables into an instantaneous rain rate is fraught with uncertainties.One of those uncertainties is the varying relationship of radar observables to rain rate for different regions and storm types due to variations in drop size distributions. Many unique reflectivity-to-rain rate (Z-R) functions have been proposed in the literature over the past 70 years for single-polarization radars, and it is becoming apparent that various rain rate functions will also be needed in different environments for dual-polarization radars as well. The challenge then becomes identifying the environments in real-time such that the appropriate rain rate function can be applied. This study addresses the challenge of identifying environments conducive for tropical rain rates, or rain rates that are enhanced by highly productive warm rain processes. Rain rates in tropical environments tend to be underestimated by other operational Z-R functions and have often been associated with historic flash flooding events, so delineating them in real-time can greatly improve not only the radar-based QPE accuracy, but the level of certainty by forecasters for issuing flash flood warnings as well.Six consecutive months of hourly data from the 2010 warm season were used to train ensembles of statistical classification models such that probabilities of warm rain enhancement of rain rate can be derived. The predictors for the ensemble were retrieved from the 20-km Rapid Update Cycle (RUC) model analyses and were chosen to provide a general description of the thermodynamic environment from the which the rainfall developed. Those environmental predictors were trained against two different predictands: bias of rain rates for the convective Z-R function vs. collocated, quality controlled rain gauges, and the vertical gradient of radar reflectivity between the freezing level and the lowest elevation observed by the radar. The resulting probabilities from the trained ensembles were then used to delineate where tropical rain rates would be assigned in a gridded QPE product, and the resulting hourly accumulations were verified against independent rain gauges.Overall, the probability-based precipitation type delineation scheme improved hourly rainfall accumulations for three independent cases tested when compared to both the legacy rainfall product from the National Mosaic and Multisensor Quantitative Precipitation Estimation (NMQ) project and the operational NWS rainfall product (Stage II), but neither the gauge-based nor VPR-based ensembles emerged as a clearly superior predictor than the other for all cases tested. However, spatial similarities between the two probability fields and similar results from variable importance analysis suggest that both methods are attempting to delineate the same environment. This implies that the systematic underestimation of radar-based QPE and the enhancement of reflectivity in the warm layer from warm rain hydrometeor growth are related or at the very least are associated with the same type of environment. Initial analysis of polarimetric variables, particularly differential reflectivity, in areas of high and low probabilities also support a connection between rain rate underestimation and tropical airmasses

Rainfall prediction in Australia : Clusterwise linear regression approach

Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been proposed in the literature, including statistical and computational intelligence approaches. However, finding a method to model the complex physical process of rainfall, especially in Australia where the climate is highly variable, is still a major challenge. The aims of this study are to: (a) develop an optimization based clusterwise linear regression method, (b) develop new prediction methods based on clusterwise linear regression, (c) assess the influence of geographic regions on the performance of prediction models in predicting monthly and weekly rainfall in Australia, (d) determine the combined influence of meteorological variables on rainfall prediction in Australia, and (e) carry out a comparative analysis of new and existing prediction techniques using Australian rainfall data. In this study, rainfall data with five input meteorological variables from 24 geographically diverse weather stations in Australia, over the period January 1970 to December 2014, have been taken from the Scientific Information for Land Owners (SILO). We also consider the climate zones when selecting weather stations, because Australia experiences a variety of climates due to its size. The data was divided into training and testing periods for evaluation purposes. In this study, optimization based clusterwise linear regression is modified and new prediction methods are developed for rainfall prediction. The proposed method is applied to predict monthly and weekly rainfall. The prediction performance of the clusterwise linear regression method was evaluated by comparing observed and predicted rainfall values using the performance measures: root mean squared error, the mean absolute error, the mean absolute scaled error and the Nash-Sutclie coefficient of efficiency. The proposed method is also compared with the clusterwise linear regression based on the maximum likelihood estimation, linear support vector machines for regression, support vector machines for regression with radial basis kernel function, multiple linear regression, artificial neural networks with and without hidden layer and k-nearest neighbours methods using computational results. Initially, to determine the appropriate input variables to be used in the investigation, we assessed all combinations of meteorological variables. The results confirm that single meteorological variables alone are unable to predict rainfall accurately. The prediction performance of all selected models was improved by adding the input variables in most locations. To assess the influence of geographic regions on the performance of prediction models and to compare the prediction performance of models, we trained models with the best combination of input variables and predicted monthly and weekly rainfall over the test periods. The results of this analysis confirm that the prediction performance of all selected models varied considerably with geographic regions for both weekly and monthly rainfall predictions. It is found that models have the lowest prediction error in the desert climate zone and highest in subtropical and tropical zones. The results also demonstrate that the proposed algorithm is capable of finding the patterns and trends of the observations for monthly and weekly rainfall predictions in all geographic regions. In desert, tropical and subtropical climate zones, the proposed method outperform other methods in most locations for both monthly and weekly rainfall predictions. In temperate and grassland zones the prediction performance of the proposed model is better in some locations while in the remaining locations it is slightly lower than the other models.Doctor of Philosoph

Using Bayesian model selection and calibration to improve the DayCent ecosystem model

2020 Fall.Includes bibliographical references.Process-based biogeochemical models have been developed and used for decades to predict the outcomes of real-world ecological processes. These models are based on a theoretical understanding of relevant ecological processes and approximated using highly complex mathematical equations and hundreds of unknown parameters—requiring calibration using physical observations of the system. These models are then used to test scientific understanding, estimate pools and fluxes, make predictions for future scenarios, and to evaluate management and policy outcomes. To provide a better understanding of the ecological processes, these models need to be simple, make accurate predictions, and account for all sources of uncertainty. The focus of this dissertation is to develop a Bayesian model analysis framework to meet the goal of developing simple and accurate models that fully address uncertainty. This framework includes variance-based global sensitivity analysis (GSA) to identify influential model parameters, a Bayesian calibration method using sampling importance resampling (SIR) to estimate the posterior distribution of unknown model parameters and hyperparameters, and a Monte Carlo analysis to estimate the posterior predictive distribution of model outputs. The framework accounts for all sources of uncertainty, including the remaining uncertainty over the fitted parameters. Additionally, Bayesian model selection is also implemented in the framework to determine the most appropriate level of complexity during model development. The framework is applied to improve the DayCent ecosystem model in agricultural applications. The DayCent model was improved with several model developments, including NH3 volatilization, the release of nitrogen (N) from controlled-release N fertilizers (CRNFs) and the inhibition of the biological process of nitrification and delay the transformation of NH+4 to NO-3 with nitrification inhibitor (NIs). The model development incorporates key 4R management practices that mitigate NH3 and N2O emissions in fertilized upland agricultural soils. In addition, I recalibrated the soil organic matter submodel to improve estimation of soil organic carbon (C) sequestration potentials to a 30 cm depth for several management practices, including organic matter amendment, adoption of no-till management, and addition of synthetic N fertilizers. The results showed that the DayCent model predictions of C sequestration and reduction in N2O flux as well as NH3 volatilization from several management practices were consistent with the field observations. The model result suggested that addition of organic amendments and adoption of no-till are viable management option for C sequestration, however, the addition of synthetic N fertilizer did not produce a significant level of C sequestration. For NH3 volatilization, the model also adequately captures the reduction potential of urease inhibitor along with the incorporation of urea by mechanical means or with immediate irrigation/rainfall. The model also shows promising results in mitigating N2O emissions with both CRNFs and NIs in comparison to field observations. The model prediction focuses on estimating greenhouse gas (GHG) mitigation potential and estimation of uncertainty arising during model prediction—enhancing DayCent as a tool for scientific understanding, regional to global assessments, policy implementation, and carbon emission trading. Overall, the model improvements enhanced the ability of the DayCent model in providing a stronger basis to support policy and management decisions associated with GHG mitigation in agricultural soils

The WWRP Polar Prediction Project (PPP)

Mission statement: “Promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours to seasonal”. Increased economic, transportation and research activities in polar regions are leading to more demands for sustained and improved availability of predictive weather and climate information to support decision-making. However, partly as a result of a strong emphasis of previous international efforts on lower and middle latitudes, many gaps in weather, sub-seasonal and seasonal forecasting in polar regions hamper reliable decision making in the Arctic, Antarctic and possibly the middle latitudes as well. In order to advance polar prediction capabilities, the WWRP Polar Prediction Project (PPP) has been established as one of three THORPEX (THe Observing System Research and Predictability EXperiment) legacy activities. The aim of PPP, a ten year endeavour (2013-2022), is to promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on hourly to seasonal time scales. In order to achieve its goals, PPP will enhance international and interdisciplinary collaboration through the development of strong linkages with related initiatives; strengthen linkages between academia, research institutions and operational forecasting centres; promote interactions and communication between research and stakeholders; and foster education and outreach. Flagship research activities of PPP include sea ice prediction, polar-lower latitude linkages and the Year of Polar Prediction (YOPP) - an intensive observational, coupled modelling, service-oriented research and educational effort in the period mid-2017 to mid-2019

Towards Direct Simulation of Future Tropical Cyclone Statistics in a High-Resolution Global Atmospheric Model

We present a set of high-resolution global atmospheric general circulation model (AGCM) simulations focusing on the model's ability to represent tropical storms and their statistics. We find that the model produces storms of hurricane strength with realistic dynamical features. We also find that tropical storm statistics are reasonable, both globally and in the north Atlantic, when compared to recent observations. The sensitivity of simulated tropical storm statistics to increases in sea surface temperature (SST) is also investigated, revealing that a credible late 21st century SST increase produced increases in simulated tropical storm numbers and intensities in all ocean basins. While this paper supports previous high-resolution model and theoretical findings that the frequency of very intense storms will increase in a warmer climate, it differs notably from previous medium and high-resolution model studies that show a global reduction in total tropical storm frequency. However, we are quick to point out that this particular model finding remains speculative due to a lack of radiative forcing changes in our time-slice experiments as well as a focus on the Northern hemisphere tropical storm seasons

Atmospheric response to zonally averaged sea surface temperatures in the North Atlantic - a model study

A less dynamically active ocean will most likely lead to a more zonal sea surface temperature (SST) distribution. In order to identify if, and how such an effect affects the atmospheric variability, idealized experiments with an Atmospheric General Circulation Model (AGCM) of intermediate complexity have been conducted. For two different seasons, the atmospheric response to a removal of the longitudinal dependence of the SSTs in the North Atlantic are investigated. The results reveal a response projecting largely on the model's positive 1st mode of intrinsic atmospheric variability characterized by the North Atlantic Oscillation (NAO). In winter, the response is more in accordance with the variability pattern referred to as the East-Atlantic Pattern (EAP). Generally it is found that the tropical part of the ocean circulation is the most important for creating changes in atmospheric circulation, both at lower and higher latitudes. In general, the local interannual atmospheric variability is decreased in most regions, but several areas also experience increased variability. We identify a tendency of the response amplitudes to increase with longer timescales.Master i Geofysikk - MeteorologiMAMN-GFMETGEOFME

Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products