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Bin Yong
,
Liliang Ren
,
Yang Hong
,
Jonathan J. Gourley
,
Xi Chen
,
Jinwei Dong
,
Weiguang Wang
,
Yan Shen
, and
Jill Hardy

Abstract

Hydrological processes in most semiarid regions on Earth have been changing under the impacts of climate change, human activities, or combinations of the two. This paper first presents a trend analysis of the spatiotemporal changes in water resources and then diagnoses their underlying atmospheric and socioeconomic causes over 10 catchments in the Laoha basin, a typical semiarid zone of northeast China. The impacts of climate variability and human activities on streamflow change were quantitatively evaluated by the VIC (Variable Infiltration Capacity) model. First, results indicate that six out of the 10 studied catchments have statistically significant downward trends in annual streamflow; however, there is no significant change of annual precipitation for all catchments. Two abrupt changes of annual streamflow at 1979 and 1998 are identified for the four largest catchments. Second, the Laoha basin generally experienced three evident dry–wet pattern switches during the past 50 years. Furthermore, this basin is currently suffering from unprecedented water shortages. Large-scale climate variability has affected the local natural hydrologic system. Third, quantitative evaluation shows human activities were the main driving factors for the streamflow reduction with contributions of approximately 90% for the whole basin. A significant increase in irrigated area, which inevitably resulted in tremendous agricultural water consumption, is the foremost culprit contributing to the dramatic runoff reduction, especially at midstream and downstream of the Laoha basin. This study is expected to enable policymakers and stakeholders to make well-informed, short-term practice decisions and better plan long-term water resource and ecoenvironment management strategies.

Full access
David M. L. Sills
,
Gregory A. Kopp
,
Lesley Elliott
,
Aaron Jaffe
,
Elizabeth Sutherland
,
Connell Miller
,
Joanne Kunkel
,
Emilio Hong
,
Sarah Stevenson
, and
William Wang
Full access
Wenting Wang
,
Hongrong Shi
,
Disong Fu
,
Mengqi Liu
,
Jiawei Li
,
Yunpeng Shan
,
Tao Hong
,
Dazhi Yang
, and
Xiang’ao Xia

Abstract

Numerical weather prediction (NWP), when accessible, is a crucial input to short-term solar power forecasting. WRF-Solar, the first NWP model specifically designed for solar energy applications, has shown promising predictive capability. Nevertheless, few attempts have been made to investigate its performance under high aerosol loading, which attenuates incoming radiation significantly. The North China Plain is a polluted region due to industrialization, which constitutes a proper testbed for such investigation. In this paper, aerosol direct radiative effect (DRE) on three surface shortwave radiation components (i.e., global, beam, and diffuse) during five heavy pollution episodes is studied within the WRF-Solar framework. Results show that WRF-Solar overestimates instantaneous beam radiation up to 795.3 W m−2 when the aerosol DRE is not considered. Although such overestimation can be partially offset by an underestimation of the diffuse radiation of about 194.5 W m−2, the overestimation of the global radiation still reaches 160.2 W m−2. This undesirable bias can be reduced when WRF-Solar is powered by Copernicus Atmosphere Monitoring Service (CAMS) aerosol forecasts, which then translates to accuracy improvements in photovoltaic (PV) power forecasts. This work also compares the forecast performance of the CAMS-powered WRF-Solar with that of the European Centre for Medium-Range Weather Forecasts model. Under high aerosol loading conditions, the irradiance forecast accuracy generated by WRF-Solar increased by 53.2% and the PV power forecast accuracy increased by 6.8%.

Significance Statement

Numerical weather prediction (NWP) is the “go-to” approach for achieving high-performance day-ahead solar power forecasting. Integrating time-varying aerosol forecasts into NWP models effectively captures aerosol direct radiation effects, thereby enhancing the accuracy of solar irradiance forecasts in heavily polluted regions. This work not only quantifies the aerosol effects on global, beam, and diffuse irradiance but also reveals the physical mechanisms of irradiance-to-power conversion by constructing a model chain. Using the North China Plain as a testbed, the performance of WRF-Solar on solar power forecasting on five severe pollution days is analyzed. This version of WRF-Solar can outperform the European Centre for Medium-Range Weather Forecasts model, confirming the need for generating high spatial–temporal NWP.

Restricted access
Hemantha W. Wijesekera
,
Conrad A. Luecke
,
David W. Wang
,
Ewa Jarosz
,
Sergio DeRada
,
William J. Teague
,
Kyung-Il Chang
,
Jae Hak Lee
,
Hong-Sik Min
, and
SungHyun Nam

Abstract

Small-scale processes at the southwestern boundary of the Ulleung Basin (UB) in the Japan/East Sea (JES) were examined using combined ship-based and moored observations along with model output. Model results show baroclinic semidiurnal tides are generated at the shelf break and corresponding slope connecting the Korea/Tsushima Strait with the UB and propagate into the UB with large barotropic-to-baroclinic energy conversion over the slope. Observations show high-frequency internal wave packets and indicate strong velocity shear and energetic turbulence associated with baroclinic tides in the stratified bottom layer. Solitary-like waves with frequencies from 0.2N to 0.5N (buoyancy frequency N) were found at the edge of the shelf break with supercritical flow. For subcritical flow, a hydraulic jump formed over the shelf break with weakly dispersive internal lee waves with frequencies varying from 0.5N to N. These high-frequency lee waves were trapped in the stratified bottom layer, with wave stress similar to the turbulent stress near the bottom. The power loss due to turbulent bottom drag can be an important factor for energy loss associated with the hydraulic jump. Turbulent kinetic energy dissipation rates of ∼10−4 W kg−1 were found. Large downward heat and salt fluxes below the high-salinity core mix warm/salty Tsushima Current Water with cold/low-salinity JES Intermediate Water. Mixing over the shelf break could be very important to the JES circulation since the calculated diapycnal upwelling (1–6 m day−1) at the shelf break and slope is substantially greater than the basin-averaged estimate from chemical tracers and modeling studies.

Significant Statement

The Japan/East Sea (JES) is a marginal sea, enclosed by Japan, Korea, and Russia. This study describes mixing processes over the shelf break connecting the northern Korea/Tsushima Strait (KTS) with the southern Ulleung Basin (UB), where the warm, high-salinity Kuroshio water carried by the Tsushima Current interacts with southward-flowing subsurface water masses in the JES. Our analysis suggests that the shelf break and slope between the KTS and the UB are vital areas for water-mass exchange in the southern JES. The enhanced mixing at the shelf break may impact water masses and circulation over the entire JES.

Open access
Chi–Sann Liou
,
Jen–Her Chen
,
Chuen–Teyr Terng
,
Feng–Ju Wang
,
Chin–Tzu Fong
,
Thomas E. Rosmond
,
Hong–Chi Kuo
,
Chih–Hui Shiao
, and
Ming–Dean Cheng

Abstract

The global forecast system (GFS), which started its operation in 1988 at the Central Weather Bureau in Taiwan, has been upgraded to incorporate better numerical methods and more complete parameterization schemes. The second-generation GFS uses multivariate optimum interpolation analysis and incremental nonlinear normal-mode initialization to initialize the forecast model. The forecast model is a global primitive equation model with a resolution of 18 sigma levels in the vertical and 79 waves of triangular truncation in the horizontal. The forecast model includes a 1.5-order eddy mixing parameterization, a gravity wave drag parameterization, a shallow convection parameterization, a relaxed version of Arakawa–Schubert cumulus parameterization, grid-scale condensation calculation, and longwave and shortwave radiative transfer calculations with consideration of fractional clouds. The performance of the second-generation GFS is significantly better than the first-generation GFS. For two 3-month periods in winter 1995/96 and summer 1996, the second-generation GFS provided forecasters with 5-day forecasts where the averaged 500-mb height anomaly correlation coefficients for the Northern Hemisphere were greater than 0.6.

Observational data available to the GFS are much less than those at other numerical weather prediction centers, especially in the Tropics and Southern Hemisphere. The GRID messages of 5° resolution, ECMWF 24-h forecast 500-mb height and 850- and 200-mb wind fields available once a day on the Global Telecommunications System are used as supplemental observations to increase the data coverage for the GFS data assimilation. The supplemental data improve the GFS performance both in the analysis and forecast.

Full access
Masao Kanamitsu
,
Arun Kumar
,
Hann-Ming Henry Juang
,
Jae-Kyung Schemm
,
Wanqui Wang
,
Fanglin Yang
,
Song-You Hong
,
Peitao Peng
,
Wilber Chen
,
Shrinivas Moorthi
, and
Ming Ji

The new National Centers for Environmental Prediction (NCEP) numerical seasonal forecast system is described in detail. The new system is aimed at a next-generation numerical seasonal prediction in which focus is placed on land processes, initial conditions, and ensemble methods, in addition to the tropical SST forcing. The atmospheric model physics is taken from the NCEP–National Center for Atmospheric Research (NCAR) reanalysis model, which has more comprehensive land hydrology and improved physical processes. The model was further upgraded by introducing three new parameterization schemes: 1) the relaxed Arakawa–Schubert (RAS) convective parameterization, which improved middle latitude response to tropical heating; 2) Chou's shortwave radiation, which corrected surface radiation fluxes; and 3) Chou's longwave radiation scheme together with smoothed mean orography that reduced model warm bias. Atmospheric initial conditions were taken from the operational NCEP Global Data Assimilation System, allowing the seasonal forecast to start from realistic initial conditions and to seamlessly connect with the short- and medium-range forecasts. The Pacific basin ocean model is the same as that in the old NCEP seasonal system and is coupled to the new atmospheric model with a two-tier approach. The operational atmospheric forecast is performed once a month with a 20-member ensemble. Prior to the forecast, 10-member ensemble hindcasts of the same month from 1979 to the present are performed to define model climatology and model forecast skill. The system has been running routinely since April 2000, and the products are available online at NWS's ftp site.

Full access
David M. L. Sills
,
Gregory A. Kopp
,
Lesley Elliott
,
Aaron L. Jaffe
,
Liz Sutherland
,
Connell S. Miller
,
Joanne M. Kunkel
,
Emilio Hong
,
Sarah A. Stevenson
, and
William Wang

Abstract

Canada is a vast country with most of its population located along its southern border. Large areas are sparsely populated and/or heavily forested, and severe weather reports are rare when thunderstorms occur there. Thus, it has been difficult to accurately assess the true tornado climatology and risk. It is also important to establish a reliable baseline for tornado-related climate change studies. The Northern Tornadoes Project (NTP), led by Western University, is an ambitious multidisciplinary initiative aimed at detecting and documenting every tornado that occurs across Canada. A team of meteorologists and wind engineers collects research-quality data during each damage investigation via thorough ground surveys and high-resolution satellite, aircraft, and drone imaging. Crowdsourcing through social media is also key to tracking down events. In addition, NTP conducts research to improve our ability to detect and accurately assess tornadoes that affect forests, cropland, and grassland. An open data website allows sharing of resulting datasets and analyses. Pilot investigations were carried out during the warm seasons of 2017 and 2018, with the scope expanding from the detection of any tornadoes in heavily forested regions of central Canada in 2017 to the detection of all EF1+ tornadoes in Ontario plus all significant events outside of Ontario in 2018. The 2019 season was the first full campaign, systematically collecting research-quality tornado data across the entire country. To date, the project has found 89 tornadoes that otherwise would not have been identified, and increased the national tornado count in 2019 by 78%.

Full access
Ling-Feng Hsiao
,
Xiang-Yu Huang
,
Ying-Hwa Kuo
,
Der-Song Chen
,
Hongli Wang
,
Chin-Cheng Tsai
,
Tien-Chiang Yeh
,
Jing-Shan Hong
,
Chin-Tzu Fong
, and
Cheng-Shang Lee

Abstract

A blending method to merge the NCEP global analysis with the regional analysis from the WRF variational data assimilation system is implemented using a spatial filter for the purpose of initializing the Typhoon WRF (TWRF) Model, which has been in operation at Taiwan’s Central Weather Bureau (CWB) since 2010. The blended analysis is weighted toward the NCEP global analysis for scales greater than the cutoff length of 1200 km, and is weighted toward the WRF regional analysis for length below that. TWRF forecast experiments on 19 typhoons from July to October 2013 over the western North Pacific Ocean show that the large-scale analysis from NCEP GFS is superior to that of the regional analysis, which significantly improves the typhoon track forecasts. On the other hand, the regional WRF analysis provides a well-developed typhoon structure and more accurately captures the influence of the Taiwan topography on the typhoon circulation. As a result, the blended analysis takes advantage of the large-scale analysis from the NCEP global analysis and the detailed mesoscale analysis from the regional WRF analysis. In additional to the improved track forecast, the blended analysis also provides more accurate rainfall forecasts for typhoons affecting Taiwan. Because of the improved performance, the blending method has been implemented in the CWB operational TWRF typhoon prediction system.

Full access
Chidong Zhang
,
Aaron F. Levine
,
Muyin Wang
,
Chelle Gentemann
,
Calvin W. Mordy
,
Edward D. Cokelet
,
Philip A. Browne
,
Qiong Yang
,
Noah Lawrence-Slavas
,
Christian Meinig
,
Gregory Smith
,
Andy Chiodi
,
Dongxiao Zhang
,
Phyllis Stabeno
,
Wanqiu Wang
,
Hong-Li Ren
,
K. Andrew Peterson
,
Silvio N. Figueroa
,
Michael Steele
,
Neil P. Barton
,
Andrew Huang
, and
Hyun-Cheol Shin

Abstract

Observations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June–September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (<6 days) forecasts, but they grew rapidly with increasing lead time beyond 6 days. Non-weighted multimodel means outperformed all individual models approaching a 10-day forecast lead time. In contrast, errors in surface air temperature and relative humidity could be large in initial conditions and remained large through 10-day forecasts without much growth, and non-weighted multimodel means did not outperform all individual models. These results following the tracks of the mobile platforms are consistent with those at a fixed location. Large errors in initial condition of sea surface temperature (SST) resulted in part from the unusual Arctic surface warming in 2019 not captured by data assimilation systems used for model initialization. These errors in SST led to large initial and prediction errors in surface air temperature. Our results suggest that improving predictions of surface conditions over the Arctic Ocean requires enhanced in situ observations and better data assimilation capability for more accurate initial conditions as well as better model physics. Numerical predictions of Arctic atmospheric conditions may continue to suffer from large errors if they do not fully capture the large SST anomalies related to Arctic warming.

Full access
James Hlywiak
,
David D. Flagg
,
Xiaodong Hong
,
James D. Doyle
,
Charlotte Benbow
,
Milan Curcic
,
Basil Darby
,
William M. Drennan
,
Hans Graber
,
Brian Haus
,
Jamie MacMahan
,
David Ortiz-Suslow
,
Jesus Ruiz-Plancarte
,
Qing Wang
,
Neil Williams
, and
Ryan Yamaguchi

Abstract

Traditional atmospheric surface layer theory assumes homogeneous surface conditions. Regardless, nearly all surface layer parameterization schemes employed within numerical weather prediction models utilize the same techniques within highly heterogeneous coastal regimes as for homogeneous environments. We compare predicted surface weather and fluxes of momentum, heat, and moisture—focusing mainly on momentum—from regional simulations using the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) atmospheric model to observations collected from offshore buoys, inland flux towers, and radiosonde profiles during the Coastal Land-Air-Sea Interaction (CLASI) project throughout the summer of 2021 around Monterey Bay, California. Results reveal that modeled cross-coastal surface flux gradients are spuriously discontinuous, leading to systematically overestimated fluxes and weak winds inland of the coastline during onshore flow periods. Additionally, contrary to observations, modeled surface exchange coefficients are insensitive to wind direction on both sides of the coast, which degrades predictive skill downstream from the coastline. Over the central bay, prediction degrades when near-surface wind directions deviate from the prevailing flow direction as the parameterized stress–wind relationship fails during these cases. Predictive skill over the bay is therefore linked to variations in wind direction. Offshore of the geographically complex peninsula, systematic biases are less clear; however, bifurcations in drag coefficients based on wind direction were measured here as well. Last, increasing the horizontal grid spacing from 333 m to 3 km does not significantly affect surface layer prediction. This work highlights the need to reevaluate surface layer parameterization methods for modeling within coastal regions.

Significance Statement

Understanding surface layer weather is critical for many purposes, such as infrastructure design and weather forecasting. Within the context of numerical modeling and weather prediction, skillful forecasts of surface winds and temperature rely on accurate portrayal of the surface layer. By comparing observations collected during the Coastal Land-Air-Sea Interaction field program to numerical model solutions, we show that prediction of the surface layer fluxes of momentum, heat, and moisture break down near the coastline, which leads to biases in the predicted surface layer weather both inland and over the water. As surface layer parameterization methods across nearly all numerical models are rooted in the same practices, our results call into question the use of traditional methods near the coastline.

Free access