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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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Yue Yang
and
Xuguang Wang

Abstract

The sensitivity of convection-allowing forecasts over the continental United States to radar reflectivity data assimilation (DA) frequency is explored within the Gridpoint Statistical Interpolation (GSI)-based ensemble–variational (EnVar) system. Experiments with reflectivity DA intervals of 60, 20, and 5 min (RAIN60, RAIN20, and RAIN5, respectively) are conducted using 10 diverse cases. Quantitative verification indicates that the degree of sensitivity depends on storm features during the radar DA period. Five developing storms show high sensitivity, whereas five mature or decaying storms do not. The 20-min interval is the most reliable given its best overall performance compared to the 5- and 60-min intervals. Diagnostics suggest that the differences in analyzed cold pools (ACPs) among RAIN60, RAIN20, and RAIN5 vary by storm features during the radar DA period. Such ACP differences result in different forecast skills. In the case where RAIN20 outperforms RAIN60 and the case where RAIN5 outperforms RAIN20, assimilation of reflectivity with a higher frequency commonly produces enhanced and widespread ACPs, promoting broader storms that match better with reality than a lower frequency. In the case where RAIN5 performs worse than RAIN20, the model imbalance of RAIN5 overwhelms information gain associated with frequent assimilation, producing overestimated and spuriously fast-moving ACPs. In the cases where little sensitivity to the reflectivity DA frequency is found, similar ACPs are produced.

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Baosheng Li
,
Lei Zhou
,
Tao Lian
,
Ting Liu
,
Jianhuang Qin
,
Yan Du
, and
Dake Chen

Abstract

The northward-propagating monsoon intraseasonal oscillation (MISO) is the most pronounced variability over the tropical Indian Ocean during the Indian summer monsoon (June–September). MISO is accompanied by significant air–sea interactions; however, the mechanism of the oceanic feedback to MISO is still a great scientific challenge. In this study, the role of the intraseasonal sea surface temperature (SST) gradient in MISO is diagnosed using reanalysis products and model sensitivity experiments. It is found that the positive meridional gradient of intraseasonal SST induces positive wind convergence in the planetary boundary layer (PBL) and leads convection by about 1–2 days. This accounts for approximately half of the total wind convergence in the PBL to the north of convection during MISO. The warm SST anomalies before convection accelerate the intraseasonal northerly wind in the PBL due to the enhanced downward transport of momentum from aloft. By contrast, the cold SST anomalies behind deep convection weaken the downward vertical momentum transport, thereby inducing a deceleration in the intraseasonal northerly. Consequently, changes in the speed of intraseasonal northerly along the meridional direction strengthen the PBL wind convergence ahead of deep convection. It finally results in the intensification of intraseasonal rainfall associated with MISO over the summer monsoon region. In addition, a strong (weak) SST meridional gradient at an intraseasonal time scale amplifies (lessens) the MISO intensity in the model simulation. Thus, this study highlights the role of SST meridional gradient in the feedback to MISO, which differs from the weak contribution of the warm SST itself to the wind convergence mentioned in previous studies. Collectively, these findings indicate that consideration of oceanic feedback is necessary to improve the understanding and simulation of MISO.

Open access
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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Michael J. Foster
,
Coda Phillips
,
Andrew K. Heidinger
,
Eva E. Borbas
,
Yue Li
,
W. Paul Menzel
,
Andi Walther
, and
Elisabeth Weisz

Abstract

A new version of the PATMOS-x multidecadal cloud record, version 6.0, has been produced and is available from the NOAA National Centers for Environmental Information. A description of the processes and methods used for generating the dataset are presented, with a focus on the differences between version 6.0 and the previous version of PATMOS-x, version 5.3. The new version appears both to be more stable, with less intersatellite variability, and to have more consistent polar cloud detection, phase distribution, and cloud-top height distribution when compared against the MODIS EOS record. Improvements in consistency and performance are attributed to the addition of multidimensional variables for cloud detection, constraining cloud retrievals to radiometric bands available throughout the record, and the addition of data from the HIRS instrument.

Significance Statement

The PATMOS-x project produces multidecadal cloudiness records from polar-orbiting satellites. Version 6.0 combines imager and sounder data from 15 satellites and shows significant improvements in accuracy and stability.

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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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Gleb Panteleev
,
Max Yaremchuk
, and
Oceana Francis

Abstract

We analyzed the feasibility of the reconstruction of the spatially varying rheological parameters through the four-dimensional variational data assimilation of the sea ice velocity, thickness, and concentration into the viscoplastic (VP) sea ice model. The feasibility is assessed via idealized variational data assimilation experiments with synthetic observations configured for a 1-day data assimilation window in a 50 × 40 rectangular basin forced by the open boundaries, winds, and ocean currents and should be viewed as a first step in the developing the similar algorithms which can be applied for the more advanced sea ice models. It is found that “true” spatial variability (∼5.8 kN m−2) of the internal maximum ice strength parameter P * can be retrieved from observations with reasonable accuracy of 2.3–5.3 kN m−2, when an observation of the sea ice state is available daily in each grid point. Similar relative accuracy was achieved for the reconstruction of the compactness strength parameter α. The yield curve eccentricity e is found to be controlled by the data with less efficiency, but the spatial mean value of e could be still reconstructed with a similar degree of confidence. The accuracy of P * , α, and e retrievals is found to increase in regions of stronger ice velocity convergence, providing prospects for better processing of the observations collected during the recent MOSAiC experiment. The accuracy of the retrievals strongly depends on the number of the control variables characterizing the rheological parameter fields.

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Mika P. Malila
,
Francesco Barbariol
,
Alvise Benetazzo
,
Øyvind Breivik
,
Anne Karin Magnusson
,
Jim Thomson
, and
Brian Ward

Abstract

Wave crests of unexpected height and steepness pose a danger to activities at sea, and long-term field measurements provide important clues for understanding the environmental conditions that are conducive to their generation and behavior. We present a novel dataset of high-frequency laser altimeter measurements of the sea surface elevation gathered over a period of 18 years from 2003 to 2020 on an offshore platform in the central North Sea. Our analysis of crest height distributions in the dataset shows that mature, high sea states with high spectral steepness and narrow directional spreading exhibit crest height statistics that significantly deviate from standard second-order models. Conversely, crest heights in developing sea states with similarly high steepness but wide directional spread correspond well to second-order theory adjusted for broad frequency bandwidth. The long-term point time series measurements are complemented with space–time stereo video observations from the same location, collected during five separate storm events during the 2019/20 winter season. An examination of the crest dynamics of the space–time extreme wave crests in the stereo video dataset reveals that the crest speeds exhibit a slowdown localized around the moment of maximum crest elevation, in line with prevailing theory on nonlinear wave group dynamics. Extending on previously published observations focused on breaking crests, our results are consistent for both breaking and nonbreaking extreme crests. We show that wave crest steepness estimated from time series using the linear dispersion relation may overestimate the geometrically measured crest steepness by up to 25% if the crest speed slowdown is not taken into account.

Significance Statement

Better understanding of the statistics and dynamical behavior of extreme ocean surface wave crests is crucial for improving the safety of various operations at sea. Our study provides new, long-term field evidence of the combined effects of wave field steepness and directionality on the statistical distributions of crest heights in storm conditions. Moreover, we show that the dynamical characteristics of extreme wave crests are well described by recently identified nonlinear wave group dynamics. This finding has implications, for example, for wave force calculations and the treatment of wave breaking in numerical wave models.

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Mei Hou
,
Lan Cuo
,
Amirkhamza Murodov
,
Jin Ding
,
Yi Luo
,
Tie Liu
, and
Xi Chen

Abstract

Transboundary rivers are often the cause of water-related international disputes. One example is the Amu Darya River, with a catchment area of 470 000 km2, which passes through five countries and provides water resources for 89 million people. Intensified human activities and climate change in this region have altered hydrological processes and led to water-related conflicts and ecosystem degradation. Understanding streamflow composition and quantifying the change impacts on streamflow in the Amu Darya basin (ADB) are imperative to water resources management. Here, a degree-day glacier-melt scheme coupled offline with the Variable Infiltration Capacity hydrological model (VIC-glacier), forced by daily precipitation, maximum and minimum air temperature, and wind speed, is used to examine streamflow composition and changes during 1953–2019. Results show large differences in streamflow composition among the tributaries. There is a decrease in the snowmelt component (−260.8 m3 s−1) and rainfall component (−30.1 m3 s−1) at Kerki but an increase in the glacier melt component (160.0 m3 s−1) during drought years. In contrast, there is an increase in the snowmelt component (378.6 m3 s−1) and rainfall component (12.0 m3 s−1) but a decrease in the glacier melt component (−201.8 m3 s−1) during wet years. Using the VIC-glacier and climate elasticity approach, impacts of human activities and climate change on streamflow at Kerki and Kiziljar during 1956–2015 are quantified. Both methods agree and show a dominant role played by human activities in streamflow reduction, with contributions ranging 103.2%–122.1%; however, the contribution of climate change ranges from −22.1% to −3.2%.

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