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Andrew Heymsfield
,
Aaron Bansemer
,
Gerald Heymsfield
,
David Noone
,
Mircea Grecu
, and
Darin Toohey

Abstract

Coincident radar data with Doppler radar measurements at X, Ku, Ka, and W bands on the NASA ER-2 aircraft overflying the NASA P3 aircraft acquiring in-situ microphysical measurements are used to characterize the relationship between radar measurements and ice microphysical properties. The data were obtained from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS. Direct measurements of the condensed water content as well as coincident Doppler radar measurements were acquired, facilitating improved estimates of ice particle mass, a variable that is an underlying factor for calculating and therefore retrieving the radar reflectivity (Ze ), median mass diameter (Dm ), particle terminal velocity, and snowfall rate (S). The relationship between the measured ice water content (IWC) and that calculated from the particle size distributions (PSD) using relationships developed in earlier studies, and between the calculated and measured radar reflectivity at the four radar wavelengths, are quantified. Relationships are derived between the measured IWC and properties of the PSD, Dm , Ze at the four radar wavelengths and the dual-wavelength ratio. Because IWC and Ze are measured directly, the coefficients in the mass-dimensional relationship that best match both the IWC and Ze are derived. The relationships developed here, and the mass-dimensional relationship that uses both the measured IWC and Ze to find a best match for both variables, can be used in studies that characterize the properties of wintertime snow clouds.

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Lualawi Mareshet Admasu
,
Luke Grant
, and
Wim Thiery

Abstract

Statistical and dynamical modelling techniques are used to downscale Global Climate Model (GCM) outputs to practical resolutions for local- or regional-scale applications. Current techniques do not incorporate the effects of land use and land cover changes (LULCC), although research has shown that such changes can substantially affect climate locally. Here, we explore a new downscaling technique that uses tile-level GCM outputs provided under the Coupled Model Intercomparison Project phase 6 (CMIP6). The method, land cover tile downscaling (LTD), spatially locates the tile-level GCM outputs by mapping them to corresponding classes in high-resolution land cover maps. Furthermore, it applies an elevation-based correction to account for the effect of topography on the local climate. LTD is applied to near-surface temperature outputs from CESM2 and UKESM1, and surface temperature output from CESM2 and evaluated against observations. Compared with grid-averaged control data, LTD outputs show an overall bias reduction that are not spatially consistent. Moreover, LTD performs better on air temperature data than surface temperature and areas dominated by primary/secondary land and crops than urban land. This could arise from simplifications in methods, like land cover reclassification and simplified lapse rate estimates. However, the difference in response between the two variables and land cover types imply that biases also stem from model structural features involved in estimating their tile-level outputs. This is supported by the differences between grid average data provided by the models and the same data reconstructed from tile-level outputs. Therefore, a thorough evaluation and quality control of tile-level outputs is recommended.

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Richard M. Schulte
,
Christian D. Kummerow
,
Stephen M. Saleeby
, and
Gerald G. Mace

Abstract

There are many sources of uncertainty in satellite precipitation retrievals of warm rain. In this paper, the second of a two-part study, we focus on uncertainties related to spatial heterogeneity and surface clutter. A cloud resolving model simulation of warm, shallow clouds is used to simulate satellite observations from 3 theoretical satellite architectures – one similar to the Global Precipitation Measurement Core Observatory, one similar to CloudSat, and one similar to the planned Atmosphere Observing System (AOS). Rain rates are then retrieved using a common optimal estimation framework. For this case, retrieval biases due to nonuniform beam filling are quite large, with retrieved rain rates biased low by as much as 40-50% (depending on satellite architecture) at 5 km horizontal resolution. Surface clutter also acts to negatively bias retrieved rain rates. Combining all sources of uncertainty, the theoretical AOS satellite is found to outperform CloudSat in terms of retrieved surface rain rate, with a bias of −19% compared to −28%, a reduced spread of retrieval errors, and an additional 17.5 % of cases falling within desired uncertainty limits. The results speak to the need for additional high resolution modeling simulations of warm rain in order to better characterize the uncertainties in satellite precipitation retrievals.

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Jared A. Lee
,
Pedro A. Jiménez
,
Jimy Dudhia
, and
Yves-Marie Saint-Drenan

Abstract

Aerosol optical depth (AOD) is a primary source of solar irradiance forecast error in clear-sky conditions. Improving the accuracy of AOD in NWP models like WRF will thus reduce error in both direct normal irradiance (DNI) and global horizontal irradiance (GHI), which should improve solar power forecast errors, at least in cloud-free conditions. In this study clear-sky GHI and DNI was analyzed from four configurations of the WRF-Solar model with different aerosol representations: 1) The default Tegen climatology; 2) Imposing AOD forecasts from the GEOS-5 model; 3) Imposing AOD forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) model; and 4) The Thompson-Eidhammer aerosol-aware water/ice-friendly aerosol climatology. Over eight months of these 15-min output forecasts are compared against high-quality irradiance observations at NOAA SURFRAD and Solar Radiation (SOLRAD) stations located across CONUS. In general, WRF-Solar with GEOS-5 AOD had the lowest errors in clear-sky DNI, while WRF-Solar with CAMS AOD had the highest errors, higher even than the two aerosol climatologies, which is consistent with validation of the four AOD550 datasets against AERONET stations. For clear-sky GHI, the statistics differed little between the four models, as expected due to the lesser sensitivity of GHI to aerosol loading. Hourly-average clear-sky DNI and GHI was also analyzed, and additionally compared with CAMS model output directly. CAMS irradiance performed competitively with the best WRF-Solar configuration (with GEOS-5 AOD). The markedly different performance of CAMS versus WRF-Solar with CAMS AOD indicates that CAMS is apparently less sensitive to AOD550 than WRF-Solar is.

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Gen Tolhurst
,
Pandora Hope
,
Luke Osburn
, and
Surendra Rauniyar

Abstract

Over the past century, precipitation totals in Australia's south-eastern state of Victoria have shown multi-decadal variability without clear trends. This has impacted agriculture, water security, ecosystem services, and flood hazards. Hydrological and meteorological evidence suggests that Victorian precipitation regimes have changed since the beginning of the Millennium Drought in 1997. Until now, Victorian precipitation intensity distributions have not been assessed in detail. We assess the time-varying aspect of observed precipitation intensity distributions by identifying temporal shifts in Victorian precipitation and using those different epochs to assess multi-decadal changes in precipitation characteristics. We used 788 manual rain gauges and 49 automatic weather stations to analyse sub-daily to multi-day precipitation distributions from 1900 to 2020 for three Victorian regions and four seasons. Distributions are significantly different for the three epochs (1900-1945, 1946-1996, and 1997-2020). We summarised precipitation distributions by categorising precipitation intensities, calculating histograms, and fitting gamma distributions.

This study provides evidence that Victorian precipitation distributions have shifted over decades, and distributions depend on regional and seasonal differences. Recent precipitation declines are mostly due to decreasing light and moderate precipitation, despite increasing heavy precipitation. Heavy precipitation has shown a tendency to increase in frequency since 1997. Increases were greatest for six-hour springtime and summertime precipitation in northern Victoria and wintertime precipitation in southern and eastern Victoria. Observed precipitation distributions show changes that are consistent with climate projections. To better understand processes driving observed and projected changes to precipitation distributions globally, interdecadal shifts, seasonal variations, and regional climates need to be considered.

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Caitlyn McAllister
,
Aaron Stephens
, and
Shawn M. Milrad
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P. T. May
,
B. Trewin
,
J. R. Nairn
,
B. Ostendorf
,
Chun-Hsu Su
, and
A. Moise

Abstract

This work examines the diurnal and seasonal variability of near-surface temperature and humidity at several large areas with high population density within the Maritime Continent using the Bureau of Meteorology Atmospheric Regional Reanalysis (BARRA) 12-km-resolution dataset that covers the period 1990–2019. The diurnal cycle is examined in detail, with a key feature being the relatively small diurnal variation of the wet-bulb temperature T WB when compared with the temperature and dewpoint temperature TD . The diurnal variability is strongly modulated by the monsoons with their increased rainfall and cloud cover. The near-surface signals associated with the Madden–Julian oscillation across the domains are relatively weak. Dry and humid temperature extremes are examined for regional and seasonal variability. The dry and moist variable extremes occur at different times of year, but each have spatially coherent structure.

Significance Statement

This paper examines the climatological variations of near-surface temperature and humidity and their extremes in four locations in the “Maritime Continent.” This is important because there are significant variations potentially affecting human and ecosystem health and its resilience to climate change.

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S.C. Pryor
,
J.J. Coburn
,
R.J. Barthelmie
, and
T.J. Shepherd

Abstract

New simulations at 12 km grid-spacing with the Weather and Research Forecasting (WRF) model nested in the MPI Earth System Model (ESM) are used to quantify possible changes in wind power generation potential as a result of global warming. Annual capacity factors (CF, measures of electrical power production) computed by applying a power curve to hourly wind speeds at wind turbine hub-height from this simulation are also used to illustrate the pitfalls in seeking to infer changes in wind power generation directly from low spatial resolution and time averaged ESM output. WRF-derived CF are evaluated using observed daily CF from operating wind farms. The spatial correlation coefficient between modeled and observed mean CF is 0.65 and the root mean square error is 5.4 percentage points. Output from the MPI-WRF model chain also captures some of the seasonal variability and the probability distribution of daily CF at operating wind farms. Projections of mean annual CF (CFA) indicate no change to 2050 in the Southern Great Plains and Northeast. In the Midwest inter-annual variability of CFA increases and CFA decline by up to 2 percentage points in the Northern Great Plains. The probability of wind droughts (extended periods with anomalously low production) and wind bonus periods (high production) remains unchanged over most of the eastern U.S.. The probability of wind bonus periods exhibit some evidence of higher values over the Midwest in the 2040's, while the converse is true over the Northern Great Plains.

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S.C. Pryor
,
F. Letson
,
T. Shepherd
, and
R.J. Barthelmie

Abstract

The Southern Great Plains (SGP) exhibits a relatively high frequency of periods with extremely high rainfall rates (RR) and hail. Seven months of 2017 are simulated using the Weather Research and Forecasting (WRF) model applied at convection permitting resolution with the Mibrandt-Yau microphysics scheme. Simulation fidelity is evaluated, particularly during intense convective events, using data from ASOS stations, dual-polarization RADAR, gridded data sets and observations at the DoE Atmospheric Radiation Measurement site. The spatial gradients and temporal variability of precipitation and the cumulative density functions for both RR and wind speeds exhibit fidelity. Odds ratios >1 indicate WRF is also skillful in simulating high composite reflectivity (cREF, used as a measure of widespread convection) and RR > 5 mmhr−1 over the domain. Detailed analyses of the ten days with highest spatial coverage of cREF >30 dBZ show spatially similar reflectivity fields and high RR in both RADAR data and WRF simulations. However, during periods of high reflectivity, WRF exhibits a positive bias in terms of very high RR (> 25 mmhr−1) and hail occurrence, and during the summer and transition months, maximum hail size is underestimated. For some renewable energy applications fidelity is required with respect to the joint probabilities of wind speed and RR and/or hail. While partial fidelity is achieved for the marginal probabilities, performance during events of critical importance to these energy applications is currently not sufficient. Further research into optimal WRF configurations in support of potential damage quantification for these applications is warranted.

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Michael G. Sanderson
,
Marta Teixeira
, and
António Graça

Abstract

Cold air pools can have several different impacts on viticulture, including final grape quality and yields. This study focuses on cold pools in the upper Douro valley, which is one of the most important viticultural regions of northern Portugal. First, digital elevation model data were analyzed to identify pixels corresponding to the valley floors of the Douro and selected side valleys. Next, the topographic amplification factor was calculated for each of these pixels. Down-valley gradients in the topographic amplification factor were used to identify locations where cold air in the valley was likely to pool. High time resolution meteorological data recorded between January 2011 and December 2017 were analyzed to identify cold pool events at one location in the main Douro valley. The cold pools were assigned to seven different categories based on their temporal behavior. There was a clear seasonal cycle in numbers of cold pools, with most seen during winter and the fewest in summer. The maximum strengths of the cold pools could occur at any time during the night, although the majority peaked around the middle of the night. This study is believed to be the first to examine cold pools in the upper Douro valley.

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