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Shyama Mohanty
,
Madhusmita Swain
,
Raghu Nadimpalli
,
K. K. Osuri
,
U. C. Mohanty
,
Pratiman Patel
, and
Dev Niyogi

Abstract

The city of Mumbai, India, frequently receives extreme rainfall (>204.5 mm day−1) during the summer monsoonal period (June–September), causing flash floods and other hazards. An assessment of the meteorological conditions that lead to these rain events is carried out for 15 previous cases from 1980 to 2020. The moisture source for such rain events over Mumbai is generally an offshore trough, a midtropospheric cyclone, or a Bay of Bengal depression. The analysis shows that almost all of the extreme rain events are associated with at least two of these conditions co-occurring. The presence of a narrow zone of high sea surface temperature approximately along the latitude of Mumbai over the Arabian Sea can favor mesoscale convergence and is observed at least 3 days before the event. Anomalous wind remotely supplying copious moisture from the Bay of Bengal adds to the intensity of the rain event. The presence of midtropospheric circulation and offshore trough, along with the orographic lifting of the moisture, give a unique meteorological setup to bring about highly localized catastrophic extreme rainfall events over Mumbai. The approach adopted in this study can be utilized for other such locales to develop location-specific guidance that can aid the local forecasting and emergency response communities. Further, it also provides promise for using data-driven/machine learning–based pattern analysis for developing warning triggers.

Significance Statement

We have identified the meteorological conditions that lead to extreme heavy rains over Mumbai, India. They are that 1) at least two of these rain-bearing systems, offshore trough, midtropospheric circulation, and Bay of Bengal depression moving north-northwestward are concurrently present, 2) an anomalous high SST gradient is present along the same latitude as Mumbai, and 3) the Western Ghats orography favors the rainfall extreme to be highly localized over Mumbai.

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Lualawi Mareshet Admasu
,
Luke Grant
, and
Wim Thiery

Abstract

Statistical and dynamical modeling 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, 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 phase 6 of the Coupled Model Intercomparison Project (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 the Community Earth System Model, version 2 (CESM2) and U.K. Earth System Model, version 1 (UKESM1), and surface temperature output from CESM2 and evaluated against observations. In comparison with grid-averaged control data, LTD outputs show an overall bias reduction that is not spatially consistent. Moreover, LTD performs better on air temperature data than on surface temperature and better on areas dominated by primary/secondary land and crops than on 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 implies 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.

Open access
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 three 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 beamfilling are very large, with retrieved rain rates negatively (low) biased 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% as compared with −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 so as to better characterize the uncertainties in satellite precipitation retrievals.

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Joanne Kunkel
,
John Hanesiak
, and
David Sills

Abstract

Historical tornado events from 1982 to 2020 were documented within Canada’s forested regions using high-resolution satellite imagery. Tornado forest disturbances were identified using a three-step process: 1) detecting, 2) assessing, and 3) dating each event. A grid of 120 km × 120 km boxes was created covering Canada (excluding the extreme north). Of the 484 boxes, 367 were manually searched. Once a long, narrow region of tree damage was detected, it was first cross-referenced with known tornado databases to ensure it was a unique event. Once events were classified as either tornadic or downburst, the coordinates of the start, worst damage, and end locations were documented, as well as the direction of motion, damage indicators, degree of damage, estimated maximum wind speed, and F/EF-scale rating. In total, 231 previously unknown tornadoes were identified. In Ontario, 103 events were discovered, followed by 98 in Quebec, 9 in Manitoba, 6 in Saskatchewan, 9 in Alberta, 5 in British Columbia, and 1 in New Brunswick. The largest number of discovered tornadoes occurred in 2015, and the largest number of strong F2 tornadoes occurred in 2005. Most of the discovered tornadoes occurred in July for both F/EF1 and F/EF2 ratings. Most tornado tracks had widths between 200 and 400 m, and more than 50% of the tornadoes had a pathlength of less than 10 km. Of all the events that were discovered, 125 events could be fully dated, 19 were dated only by month, 41 were dated only by year, and 46 remained undated.

Open access
Virve Karsisto
and
Matti Horttanainen

Abstract

Forecasting road conditions is important, especially in areas with wintry conditions and rapidly changing weather. Accurate forecasts help authorities keep roads safe and optimize maintenance. Considering local features is important when making the forecast because the road surface temperature can vary significantly depending on the road surroundings. For example, in a shadowed location, the road surface temperature can be significantly lower than in open surroundings. A road weather model developed at the Finnish Meteorological Institute is used to forecast the road surface temperature and road conditions. However, the model still assumes open road surroundings. In this study, sky view factor and screening are included in the model, and their effects on the forecast road surface temperature is tested. Road surface temperature hindcasts were performed for 23 selected road weather stations in Finland for three winter periods (October–March) between 2018 and 2021. The results were location dependent, and even changing the lane had a great effect on the verification results in some cases. At best, the screening considerably decreased RMSE values during the day. However, there were many cases in which the screening increased RMSE. In general, the used shadowing algorithm increased the already negative bias during the day. Nevertheless, there were also cases in which the shadowing algorithm improved the bias, especially in February. During the night, the sky view factor made the forecast generally a little warmer, which often slightly decreased the negative bias in the forecast.

Significance Statement

The screening caused by objects surrounding a road has a great effect on the road surface temperature. Recently, a screening algorithm was added to the Finnish Meteorological Institute’s model that forecasts road conditions. The purpose of this study was to test how the algorithm affects the accuracy of road surface temperature forecasts. According to the results, the screening greatly improved the forecast accuracy in some cases. However, in some cases, the screening made the already overly cold forecast even colder. The study has increased our understanding of the effect of shadowing in the modeled road surface temperatures and helps to create more accurate forecasts in the future.

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

Abstract

The Southern Great Plains (SGP) region 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 Milbrandt–Yau microphysics scheme. Simulation fidelity is evaluated, particularly during intense convective events, using data from ASOS stations, dual-polarization radar, and gridded datasets 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 that WRF is also skillful in simulating high composite reflectivity (cREF, used as a measure of widespread convection) and RR > 5 mm h−1 over the domain. Detailed analyses of the 10 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 mm h−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.

Significance Statement

Heavy rainfall and hail during convective events are challenging for numerical models to simulate in both space and time. For some applications, such as to estimate damage to wind turbine blades and solar panels, fidelity is also required with respect to hail size and joint probabilities of wind speed and hydrometeor type and rainfall rates (RR). This demands fidelity that is seldom evaluated. We show that, although this simulation exhibits fidelity for the marginal probabilities of wind speed, RR, and hail occurrence, the joint probabilities of these properties and the simulation of maximum size of hail are, as yet, not sufficient to characterize potential damage to these renewable energy industries.

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Qian-Jin Zhou
,
Lei Li
,
Pak-Wai Chan
,
Xue-Ling Cheng
,
Chang-Xing Lan
,
Jia-Chen Su
,
Yu-Qing He
, and
Hong-Long Yang

Abstract

Supertyphoons (STs) and strong convection gales (SCGs) are extremely hazardous weather events over land. Knowledge of their processes is crucial for various applications, such as intensity forecasts of gales and the design of high-rise construction and infrastructure. Here, an observational analysis of two strong SCGs and two STs is presented based on data from the Shenzhen meteorological gradient tower, the tallest in Asia. Differences in the intrinsic physical characteristics measured at each event can be associated with different disaster-causing mechanisms. Wind speeds during STs are comparatively much larger but feature slower variations, while those of SCGs are more abrupt. Unlike that observed during STs, the vertical distribution of wind speeds during SCGs obeys a power law or exponential distribution only within 1-h maximum wind speed windows. In comparison with a Gaussian distribution, a generalized extreme value distribution can better characterize the statistical characteristics of the gusts of both STs and SCGs events. Deviations from Kolmogorov’s −5/3 power law were observed in the energy spectra of both phenomena at upper levels, albeit with differences. Different from what is seen in the ST energy spectrum distribution, a clear process of energy increase and decrease could be seen in SCGs during gale evolution. Nonetheless, both SCGs and STs exhibited a high downward transfer of turbulent momentum flux at a 320 m height, which could be attributed to the pulsation of the gusts rather than to the large-scale base flow.

Significance Statement

Strong gales induced by typhoons and severe convection have potential serious impacts on human society. The current study compares and analyzes the characteristics of the gales induced by the two different weather systems using the data observed by a 356-m-tall tower in South China. This paper also shows the relationship between gusts of the near-surface wind and the turbulent momentum fluxes, thus suggesting a possible mechanism leading to destructive forces in surface winds. In terms of social value, this study would contribute to increase the awareness of gales (the instantaneous wind speed over 17 m s−1) and improve the prediction and prevention of different types of gales, as well as the wind-resistant design of high-rise buildings.

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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 (CF A ) indicate no change to 2050 in the southern Great Plains and Northeast. Interannual variability of CF A increases in the Midwest, and CF A declines 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 United States. The probability of wind bonus periods exhibits some evidence of higher values over the Midwest in the 2040s, whereas the converse is true over the northern Great Plains.

Significance Statement

Wind energy is playing an increasingly important role in low-carbon-emission electricity generation. It is a “weather dependent” renewable energy source, and thus changes in the global atmosphere may cause changes in regional wind power production (PP) potential. We use PP data from operating wind farms to demonstrate that regional simulations exhibit skill in capturing actual power production. Projections to the middle of this century indicate that over most of North America east of the Rocky Mountains annual expected PP is largely unchanged, as is the probability of extended periods of anomalously high or low production. Any small declines in annual PP are of much smaller magnitude than changes due to technological innovation over the last two decades.

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Jacob Coburn
and
Sara C. Pryor

Abstract

Capacity factors (CFs) derived from daily expected power at 22 operating wind farms in different regions of North America are used as predictands to train statistical downscaling algorithms using output from ERA5. The statistical downscaling models are then used to make CF projections for a suite of CMIP6 Earth System Models (ESMs). Downscaling is performed using a hybrid statistical approach that employs synoptic types derived using k-means clustering applied to sea level pressure fields with variance corrections applied as a function of the pressure gradient intensity. ESMs exhibit marked variability in terms of the skill with which the frequency of synoptic types and pressure gradients are reproduced relative to ERA5, and that differential skill is used to infer differential credibility in the associated CF projections. Projections of median annual mean CF [P50(CF)] in each 20-yr period from 1980 to 2099 show evidence of declines at most wind farms except in parts of the southern Great Plains, although the magnitude of the changes is strongly dependent on the ESM. For example, P50(CF) in 2080–99 deviate from those in 1980–99 by from −3.1 to +0.2 percentage points in the Northeast. The largest-magnitude declines in P50(CF) ranging from −3.9 to −2 percentage points are projected for the southern West Coast. CF trends exhibit marked seasonality and are strongly linked to changes in the relative intensity of future synoptic patterns, with much less impact from shifts in the occurrence of synoptic types over time. Internal climate modes continue to play a significant role in inducing interannual variability in wind power production, even under high radiative forcing scenarios.

Significance Statement

We describe how future climate changes may affect wind resources and wind power generation. Near-term changes in projected wind power electricity generation potential at operating wind farms over North America are small, but by the end of the current century electricity production is projected to decrease in many areas but may increase in parts of the southern Great Plains. The amount of change in projected wind power production is a strong function of the Earth system model that is downscaled and also depends on the continued presence of internally forced climate variability. An additional dependence on the amount of greenhouse gas–induced global warming indicates the transition of the energy sector to low-carbon sources may assist in maintaining the abundant U.S. wind resource.

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Anna N. Kaminski
,
Jason M. Cordeira
,
Nicholas D. Metz
,
Katie Bachli
,
Megan Duncan
,
Michaela Ericksen
,
Ivy Glade
,
Cassandra Roberts
, and
Clark Evans

Abstract

Atmospheric rivers (ARs) are a frequently studied phenomenon along the West Coast of the United States, where they are typically associated with the heaviest local flooding events and almost one-half of the annual precipitation totals. By contrast, ARs in the northeastern United States have received considerably less attention. The purpose of this study is to utilize a unique visual inspection methodology to create a 30-yr (1988–2017) climatology of ARs in the northeastern United States. Consistent with its formal definition, ARs are defined as corridors with integrated vapor transport (IVT) values greater than 250 kg m−1 s−1 over an area at least 2000 km long but less than 1000 km wide in association with an extratropical cyclone. Using MERRA2 reanalysis data, this AR definition is used to determine the frequency, duration, and spatial distribution of ARs across the northeastern United States. Approximately 100 ARs occur in the northeastern United States per year, with these ARs being quasi-uniformly distributed throughout the year. On average, northeastern U.S. ARs have a peak IVT magnitude between 750 and 999 kg m−1 s−1, last less than 48 h, and arrive in the region from the west to southwest. Average AR durations are longer in summer and shorter in winter. Further, ARs are typically associated with lower IVT in winter and higher IVT in summer. Spatially, ARs more frequently occur over the Atlantic Ocean coastline and adjacent Gulf Stream waters; however, the frequency with which large IVT values are associated with ARs is highest over interior New England.

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