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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
L. Cucurull
and
R. J. Purser

Abstract

Under very large vertical gradients of atmospheric refractivity, which are typical at the height of the planetary boundary layer, the assimilation of radio-occultation (RO) observations into numerical weather prediction (NWP) models presents several serious challenges. In such conditions, the assimilation of RO bending angle profiles is an ill-posed problem, the uncertainty associated with the RO observations is higher, and the one-dimensional forward operator used to assimilate these observations has several theoretical deficiencies. As a result, a larger percentage of these RO observations are rejected at the NWP centers by existing quality control procedures, potentially limiting the benefits of this data type to improve weather forecasting in the lower troposphere. To address these problems, a new methodology that enables the assimilation of RO data to be extended to the lower moist troposphere has been developed. Challenges associated with larger atmospheric gradients of refractivity are partially overcome by a reformulation that has minimal effect at higher altitudes. As a first step towards this effort, this study presents both the theoretical development of this new methodology and a forecast impact assessment of it using the NCEP NWP system. Though using a conservative approach, benefits in the lower tropical troposphere are already noticeable. The encouraging results of this work open the potential for further exploitation and optimization of RO assimilation.

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Nigel Roberts
,
Benjamin Ayliffe
,
Gavin Evans
,
Stephen Moseley
,
Fiona Rust
,
Caroline Sandford
,
Tomasz Trzeciak
,
Paul Abernethy
,
Laurence Beard
,
Neil Crosswaite
,
Ben Fitzpatrick
,
Jonathan Flowerdew
,
Tom Gale
,
Leigh Holly
,
Aaron Hopkinson
,
Katharine Hurst
,
Simon Jackson
,
Caroline Jones
,
Ken Mylne
,
Christopher Sampson
,
Michael Sharpe
,
Bruce Wright
,
Simon Backhouse
,
Mark Baker
,
Daniel Brierley
,
Anna Booton
,
Clare Bysouth
,
Robert Coulson
,
Sean Coultas
,
Ric Crocker
,
Roger Harbord
,
Kathryn Howard
,
Teressa Hughes
,
Marion Mittermaier
,
Jon Petch
,
Tim Pillinger
,
Victoria Smart
,
Eleanor Smith
, and
Mark Worsfold

Abstract

The Met Office in the UK has developed a completely new probabilistic post-processing system called IMPROVER to operate on outputs from its operational Numerical Weather Prediction (NWP) forecasts and precipitation nowcasts. The aim is to improve weather forecast information to the public and other stakeholders whilst better exploiting the current and future generations of underpinning kilometer-scale NWP ensembles. We wish to provide seamless forecasts from nowcasting to medium range, provide consistency between gridded and site-specific forecasts and be able to verify every stage of the processing. The software is written in a modern modular framework that is easy to maintain, develop and share. IMPROVER allows forecast information to be provided with greater spatial and temporal detail and a faster update frequency than previous post-processing. Independent probabilistic processing chains are constructed for each meteorological variable consisting of a series of processing stages that operate on pre-defined grids and blend outputs from several NWP inputs to give a frequently updated, probabilistic forecast solution. Probabilistic information is produced as standard, with the option of extracting a most likely or yes/no outcome if required. Verification can be performed at all stages, although it is only currently switched on for the most significant stages when run in real time. IMPROVER has been producing real-time output since March 2021 and became operational in Spring 2022.

Full access
Bo Pang
,
Adam A. Scaife
,
Riyu Lu
,
Rongcai Ren
, and
Xiaoxuan Zhao

Abstract

This study investigates the interdecadal variation of the Scandinavian (SCA) pattern and corresponding drivers during the boreal winter. It is found that the SCA pattern experiences a prominent regime shift from its negative to positive phase in the early 2000s based on several reanalyses. This interdecadal change contributes to an extensive cooling over Siberia after the early 2000s, revealing its importance for recent variation of climate over Eurasia. The outputs from 35 couple models within the Coupled Model Intercomparison Projection Phase 6 (CMIP6) are also analyzed. The results show that the interdecadal change of SCA is weak in response to external forcings but can be largely explained by internal variability associated with a change of precipitation over the tropical Atlantic. Further analysis indicates that the enhanced tropical convection induces poleward propagation of Rossby waves and further results in an intensification of geopotential height over the Scandinavian Peninsula during the transition to positive SCA phases. These findings imply a contribution of tropical forcing to the observed interdecadal strengthening of SCA around the early 2000s and offer an insight into the understanding of future climate change over the Eurasian continent.

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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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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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