V. R. Kotamarthi

Argonne National Laboratory | ANL · Climate and Earth System Sciences

This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the Representative Concentration Pathway (RCP) 8.5 scenario over inland and offshore locations across the continental United States (CONUS). The proposed conditional approach extends the...

With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decision...

Atmospheric near surface wind speed and wind direction play an important role in many applications, ranging from air quality modeling, building design, wind turbine placement to climate change research. It is therefore crucial to accurately estimate the joint probability distribution of wind speed and direction. In this work we develop a conditiona...

This study examined the impact of cool roofs, green roofs, and solar panel roofs on near-surface temperature and cooling energy demand through regional modeling in the Chicago metropolitan area (CMA). The new parameterization of green roofs and solar panel roofs based on model physics has recently been developed, updated, and coupled to a multilaye...

Growth in adoption of distributed wind turbines for energy generation is significantly impacted by challenges associated with siting and accurate estimation of the wind resource. Small turbines, at hub heights of 40 m or less, are greatly impacted by terrestrial obstacles such as built structures and vegetation that can cause complex wake effects....

Global warming is expected to enhance drought extremes in the United States throughout the twenty-first century. Projecting these changes can be complex in regions with large variability in atmospheric and soil moisture on small spatial scales. Vapor Pressure Deficit (VPD) is a valuable measure of evaporative demand as moisture moves from the surfa...

Data assimilation (DA) in geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical weather prediction and is a crucial building block that has allowed dramatic improvements in weather forecasting over the past few decades. DA is commonly framed in a variation...

We discuss an approach to probabilistic forecasting based on two chained machine-learning steps: a dimensional reduction step that learns a reduction map of predictor information to a low-dimensional space in a manner designed to preserve information about forecast quantities; and a density estimation step that uses the probabilistic machine learni...

With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decision...

The second Wind Forecast Improvement Project (WFIP2) is an 18-month field campaign in the Pacific Northwest U.S.A., whose goal is to improve the accuracy of numerical-weather-prediction forecasts in complex terrain. The WFIP2 campaign involved the deployment of a large suite of in situ and remote sensing instrumentation, including eight 915-MHz win...

Data assimilation (DA) in the geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical weather prediction, and is a crucial building block that has allowed dramatic improvements in weather forecasting over the past few decades. DA is commonly framed in a vari...

This study develops a neural-network-based approach for emulating high-resolution modeled precipitation data with comparable statistical properties but at greatly reduced computational cost. The key idea is to use combination of low- and high-resolution simulations (that differ not only in spatial resolution but also in geospatial patterns) to trai...

Report on the Conference held at Argonne National Laboratory on April 14, 2021

We perform a comparative study of different supervised machine learning time-series methods for short-term and long-term temperature forecasts on a real world dataset for the daily maximum temperature over North America given by DayMET. DayMET showcases a stochastic and high-dimensional spatio-temporal structure and is available at exceptionally fi...

Abstract This study investigates changes and uncertainties to cool‐season (November‐March) storm tides along the U.S. northeast coast in the 21st century under the high RCP8.5 emission scenario compared to late 20th century. A high‐fidelity (50‐m coastal resolution) hydrodynamic storm tide model is forced with three dynamically downscaled regional...

Coupling ocean wave models to mesoscale atmospheric models is necessary to represent the effect of waves on wind turbine hub-height winds. In this report we provide a review of the most widely used ocean waves models and the phased-averaged spectral wave modeling paradigm that they are based on. Methodologies used to couple these wave models to mes...

Workshop Report

Plain Language Summary Increasing urbanization, evolving urban landscapes, and growing populations require an accurate representation of urban areas and urban processes at microscale, regional, and global scales and their feedback across the scales. Here we discuss important urban processes that should be represented in climate models and current a...

The sensitive ecosystem of the central Himalayan (CH) region, which is experiencing enhanced stress from anthropogenic forcing, requires adequate atmospheric observations and an improved representation of the Himalaya in the models. However, the accuracy of atmospheric models remains limited in this region due to highly complex mountainous topograp...

This study develops a neural network-based approach for emulating high-resolution modeled precipitation data with comparable statistical properties but at greatly reduced computational cost. The key idea is to use combination of low- and high- resolution simulations to train a neural network to map from the former to the latter. Specifically, we de...

Winter storms (e.g., nor'easters) that develop during the North American cool-season (November to March) can generate high water levels (storm tides) along the northeast coast of the U.S that can potentially result in coastal flooding. This study is concerned with how winter storm tides along the northeastern U.S. coast could change into the 21st c...

Dynamical downscaling with high-resolution regional climate models may offer the possibility of realistically reproducing precipitation and weather events in climate simulations. As resolutions fall to order kilometers, the use of explicit rather than parametrized convection may offer even greater fidelity. However, these increased model resolution...

The sensitive and fragile ecosystem of the central Himalayan (CH) region, experiencing enhanced anthropogenic pressure, requires adequate atmospheric observations and an improved representation of Himalaya in the models. However, the accuracies of atmospheric models remain limited here due to highly complex mountainous topography. This article deli...

Scientists and managers on the ground gathered to identify information gaps that pose barriers to evaluating climate change risks and responses.

Parameterizations for physical processes in weather and climate models are computationally expensive. We use model output from the Weather Research Forecast (WRF) climate model to train deep neural networks (DNNs) and evaluate whether trained DNNs can provide an accurate alternative to the physics-based parameterizations. Specifically, we develop a...

The Weather Research and Forecasting Hydrological (WRF-Hydro) system is a state-of-the-art numerical model that models the entire hydrological cycle based on physical principles. As with other hydrological models, WRF-Hydro parameterizes many physical processes. Hence, WRF-Hydro needs to be calibrated to optimize its output with respect to observat...

This seminar highlights research updates from my postdoc work at ANL.

Accurately representing flow across the mesoscale to the microscale is a persistent roadblock for completing realistic microscale simulations. The science challenges that must be addressed to coupling at these scales include the following: 1) What is necessary to capture the variability of the mesoscale flow, and how do we avoid generating spurious...

Future projections of evapotranspiration (ET) are of critical importance for agricultural and freshwater management, and predicting land-atmosphere feedbacks on the climate system. However, ET from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) simulations exhibits substantial biases, bolstering little confidence in future ET...

Parameterizations for physical processes in weather and climate models are computationally expensive. We use model output from a set of simulations performed using the Weather Research Forecast (WRF) model to train deep neural networks and evaluate whether trained models can provide an accurate alternative to the physics-based parameterizations. Sp...

Evapotranspiration (ET) is a key component of the atmospheric and terrestrial water and energy budgets. Satellite‐based vegetation index approaches have used remotely‐sensed vegetation and reanalysis meteorological properties with surface energy balance models to estimate global ET (MOD16 ET). We reconstructed satellite retrievals using in situ met...

Hight-resolved observations of vertical winds remain nearly non-existing over the Himalayas, despite of anticipated crucial role of vertical motions in transporting pollution across the Himalayan hills. The present study analyze the vertical wind observations from surface to 1 km above ground level over Manora Peak (29.4° N; 79.5° E; 1958 m amsl) i...

To realistically model a wind farm, we need to extend the range of spatial and temporal scales represented in a model from 10’s of meters to 100’s of km’s and time scales of few seconds to days. These scales in the atmosphere are represented by either mesoscale or microscale models, which have different characterizations of various dynamical and ph...

Surface hydrological models must be calibrated for each application region. The Weather Research and Forecasting Hydrological system (WRF-Hydro) is a state-of-the-art numerical model that models the entire hydrological cycle based on physical principles. However, as with other hydrological models, WRF-Hydro parameterizes many physical processes. As...

This study compares an ensemble of dynamically downscaled projections of extreme daily precipitation over the contiguous United States (CONUS). With a grid spacing of 12 km and a domain that encompasses most of North America, we use the Weather Research and Forecast model as a regional climate model. We incorporate initial and boundary conditions f...

The sensitivities of idealized large-eddy simulations (LESs) to variations of model configuration and forcing parameters on quantities of interest to wind power applications are examined. Simulated wind speed, turbulent fluxes, spectra and cospectra are assessed in relation to variations in two physical factors, geostrophic wind speed and surface r...

In the face of future climate change, it is prudent to seek sustainable adaptation strategies to address regional and local impacts. These impacts are multidimensional, involving interdependencies between systems (weather, urban land use, agriculture, etc.) that are typically modeled independently. To achieve a holistic understanding, and thus iden...

This study investigates the internal variability (IV) of a regional climate model, and considers the impacts of horizontal resolution and spectral nudging on the IV. A 16-member simulation ensemble was conducted using the Weather Research Forecasting model for three model configurations. Ensemble members included simulations at spatial resolutions...

This study uses Weather Research and Forecast (WRF) model to evaluate the performance of six dynamical downscaled decadal historical simulations with 12-km resolution for a large domain (7200 × 6180 km) that covers most of North America. The initial and boundary conditions are from three global climate models (GCMs) and one reanalysis data. The GCM...

The aim of this study is to examine projections of extreme temperatures over the continental United States (CONUS) for the 21st century using an ensemble of high spatial resolution dynamically downscaled model simulations with different boundary conditions. The downscaling uses the Weather Research and Forecast model at a spatial resolution of 12 k...

The sensitivities of idealized Large-Eddy Simulations (LES) to variations of model configuration and forcing parameters on quantities of interest to wind power applications are examined. Simulated wind speed, turbulent fluxes, spectra and cospectra are assessed in relation to variations of two physical factors, geostrophic wind speed and surface ro...

This is a user manual for the geospatial analysis tool kit developed by Argonne National Laboratory to access and analyze regional-scale climate change datasets produced as a part of a project funded by SERDP. This document serves as a companion to the two other reports produced for the SERDP-funded project RC-2242, “Climate Change Impacts at the D...

Heat and drought are two emerging climatic threats to the US maize and soybean production, yet their impacts on yields are collectively determined by the magnitude of climate change and rising atmospheric CO2 concentrations. This study quantifies the combined and separate impacts of high temperature, heat and drought stresses on the current and fut...

The weather research and forecast (WRF) model downscaling skill in extreme maximum daily temperature is evaluated by using the generalized extreme value (GEV) distribution. While the GEV distribution has been used extensively in climatology and meteorology for estimating probabilities of extreme events, accurately estimating GEV parameters based on...

We present the measurement of cloud base height (CBH) derived from the Doppler Lidar (DL), Ceilometer (CM) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite over a high altitude station in the central Himalayan region for the first time. We analyzed six cases of cloud overpass during the daytime convection period by using the clou...

We present the measurements of cloud-base height variations over ARIES, Nainital (79.45°E, 29.37°N, 1958 m amsl) obtained from Vaisala Ceilometer, during the nearly year-long Ganges Valley Aerosol Experiment (GVAX). The cloud-base measurements are analysed in conjunction with collocated measurements of rainfall, to study the possible contributions...

An extensive field study, RAWEX–GVAX, was carried out during a 10-month (June 2011–March 2012) campaign at ARIES, Nainital and observations on a wide range of parameters like physical and optical properties of aerosols, meteorological parameters and boundary layer evolution were made. This work presents results obtained from high-frequency (four la...

Climate change has great significance in Asia in general , and India in particular; and atmospheric aerosols have a decisive role in this. The climate forcing potential of aerosols is closely linked to their optical, microphysical and chemical properties. Systematic efforts to characterize these properties over the Indian region started about 5 dec...

The radiosonde humidity profiles available during the Ganges Valley Experiment were compared to those simulated from the regional Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF-Chem) and the global reanalysis datasets. Large biases were revealed. On a monthly mean basis at Nainital, located in northern India, the...

The RAWEX–GVAX field campaign has been carried out from June 2011 to March 2012 over a high altitude site Manora Peak, Nainital (29.4N; 79.2E; 1958 m amsl) in the central Himalayas to assess the impacts of absorbing aerosols on atmospheric thermodynamics and clouds. This paper presents the preliminary results of the observations and data analysis...

The RAWEX–GVAX field campaign has been carried out from June 2011 to March 2012 over a high altitude site Manora Peak, Nainital (29.4N; 79.2E; 1958 m amsl) in the central Himalayas to assess the impacts of absorbing aerosols on atmospheric ther-modynamics and clouds. This paper presents the preliminary results of the observations and data analysi...