10-m Wind Speed Research Articles

Global Synthesis of Air-Sea CO2 Transfer Velocity Estimates From Ship-Based Eddy Covariance Measurements

The air-sea gas transfer velocity (K660) is typically assessed as a function of the 10-m neutral wind speed (U10n), but there remains substantial uncertainty in this relationship. HereK660of CO2derived with the eddy covariance (EC) technique from eight datasets (11 research cruises) are reevaluated with consistent consideration of solubility and Schmidt number and inclusion of the ocean cool skin effect.K660shows an approximately linear dependence with the friction velocity (u*) in moderate winds, with an overall relative standard deviation (relative standard error) of about 20% (7%). The largest relative uncertainty inK660occurs at low wind speeds, while the largest absolute uncertainty inK660occurs at high wind speeds. There is an apparent regional variation in the steepness of theK660-u*relationships: North Atlantic ≥ Southern Ocean > other regions (Arctic, Tropics). Accounting for sea state helps to collapse some of this regional variability inK660using the wave Reynolds number in very large seas and the mean squared slope of the waves in small to moderate seas. The grand average of EC-derivedK660(−1.47 + 76.67u*+ 20.48u*2 or 0.36 + 1.203U10n+ 0.167U10n2)is similar at moderate to high winds to widely used dual tracer-basedK660parametrization, but consistently exceeds the dual tracer estimate in low winds, possibly in part due to the chemical enhancement in air-sea CO2exchange. Combining the grand average of EC-derivedK660with the global distribution of wind speed yields a global average transfer velocity that is comparable with the global radiocarbon (14C) disequilibrium, but is ~20% higher than what is implied by dual tracer parametrizations. This analysis suggests that CO2fluxes computed using aU10n2dependence with zero intercept (e.g., dual tracer) are likely underestimated at relatively low wind speeds.

Observations of boundary layer wind and turbulence of a landfalling tropical cyclone

This study investigates the atmospheric boundary layer structure based on multiple-level tower observations with a height of 350 m during the landfall of Super Typhoon Mangkhut (2018). Results show a layer of log wind profile outside of the radius of maximum wind speed with a height of 100 m or larger. The log layer height increases with the wind speed. The height of the constant flux layer reaches ~ 300 m for 10-m wind speeds less than 13 m s−1 while this height decreases with the wind speed. Momentum fluxes and turbulent kinetic energy increase with the wind speed at all vertical levels. The drag coefficient and surface roughness length estimated at the tower location have values of 7.3 × 10–3 and 0.09 m, respectively, which are independent of wind speed. The estimated vertical eddy diffusivity and mixing length increase with height up to ~ 160 m and then slowly decrease with height. The vertical eddy diffusivity increases with the wind speed while the vertical mixing length has no dependence on the wind speed. Comparing our results with previous work indicates that the vertical eddy diffusivity is larger over land than over ocean at a given wind speed range.

Comparison of the Forecast Performance of WRF Using Noah and Noah-MP Land Surface Schemes in Central Asia Arid Region

The land surface scheme (LSS) plays a very important role in the forecast of surface meteorological elements in the Central Asia arid region. Therefore, two LSSs viz. Noah and Noah-MP were evaluated over the Central Asia arid region in January and July 2017 using the Weather Research and Forecasting (WRF) model at a 3-km horizontal grid resolution. The objective was to assess the performance of WRF LSSs by calculated the mean error (ME) and the root mean squared error (RMSE) of simulated hourly meteorological elements, such as 2-m air temperature, 10-m wind speed, soil temperature, soil moisture at 5 and 25 cm thickness, and surface soil heat flux at 5 cm thickness. The results showed that, compared to Noah, Noah-MP modeled less surface sensible heat flux in the northern Xinjiang (15~20 W∙m−2) and surface latent heat flux in most areas of Xinjiang (<10 W∙m−2) in January, and mainly generated less sensible heat flux in most areas of north Xinjiang and the mountainous regions of southern Xinjiang (≤20 W·m−2) which on the contrary, generated more surface latent heat flux in most parts of Xinjiang (15~20 W·m−2) in July. Meanwhile, the surface soil heat flux generated from Noah-MP was closer to the observations at Hongliuhe and Kelameili stations in January, the ME increased by 17.5% and reduced by 80.7%, respectively, the RMSE decreased by 44.4% and 61.7%, respectively, and closer to the observations at Xiaotang station in July, the ME and RMSE reduced by 19.1% and 20.5%, respectively. Compared to Noah, Noah-MP improved the overall simulation of soil temperature and soil moisture over the northern and eastern Xinjiang (at 10 cm thickness), the ME and RMSE of simulated soil temperature reduced by 85.0% and 13.4% in January, decreased by 78.6% and increased by 6.2% in July, respectively, and the ME and RMSE of simulated soil moisture reduced by 67.2% and 14.9% in January, reduced by 33.3% and 2.8% in July, respectively. Compared to Noah, Noah-MP’s results were lowered for the simulated 10-m wind speed and 2-m air temperature, especially the simulated 2-m air temperature over the cold climate regions of northern Xinjiang, was improved significantly, the ME and RMSE of simulated 10-m wind speed reduced by 0.8% and 4.9% in January, decreased by 6.7% and 2.8% in July, respectively, the ME and RMSE of simulated 2-m air temperature reduced by 2.8% and 1.0% in July, respectively. This study demonstrated the advantage of coupled Noah-MP over the Central Asia arid region, providing the basis for WRF/Noah-MP in future operational applications in the Central Asia arid region.

Systematic improvement in simulated latent and sensible heat fluxes over tropical oceans in AMIP6 models compared to AMIP5 models with the same resolutions

Recent studies have shown that most Atmospheric Model Intercomparison Project (AMIP5) models that participated in the fifth phase of the coupled model intercomparison project (CMIP5) overestimate surface latent (QLH) and sensible (QSH) heat fluxes over tropical oceans. This study aims to quantify and understand any improvement in simulated QLH and QSH in AMIP6 models compared to AMIP5 models with the same horizontal resolutions. To accomplish this, we compare the Ocean Atmosphere air-sea Flux (OAFlux) dataset to nine AMIP5 and nine AMIP6 models integrated over 30 years (1979–2008). We found that all the AMIP5 and AMIP6 models overestimate both components of fluxes, but all of the AMIP6 models performed better than their AMIP5 counterparts over the tropical oceans (30°S-30°N). Yet, systematic spatial biases remain, which leads to only a small improvement in the simulated fluxes. For example, the root mean squared error (RMSE) and mean bias in QLH in AMIP5 are 30 and 21 W m−2 compared to 28 and 19 W m−2 in AMIP6. This is an improvement of 2 W m−2 for both RMSE and bias in QLH, yielding a reduction in QLH bias by 10% and RMSE by 7%. For QSH, an improvement of 1 W m−2 is seen in both bias and RMSE, yielding a reduction in QSH bias by 25% and RMSE by 13%. The primary reason for the improvement in QLH in AMIP6 is the better representation of the 10m wind speed and air-sea humidity difference than those in AMIP5. The improvement in QSH is due to an improvement in air-sea temperature difference. These results offer guidance to the modeling community to improve model simulation of near-surface meteorological variables in the tropics.

Wildfire Rates of Spread in Grasslands under Critical Burning Conditions

An analysis of a dataset (n = 58) of high-intensity wildfire observations in cured grasslands from southern Australia revealed a simple relationship suitable for quickly obtaining a first approximation of a fire’s spread rate under low dead fuel moisture contents and strong wind speeds. It was found that the forward rate of fire spread is approximately 20% of the average 10-m open wind speed. The data on rate of fire spread and 10 m open wind speed ranged from 1.6 to 17 and 20 to 62 km h−1, respectively. The validity of the resulting rule of thumb was examined across a spectrum of burning conditions and its performance was contrasted against that of established empirical-based fire spread models for three different grassland fuel conditions currently used operationally in Australia. The 20% rule of thumb for grassfires produced error statistics comparable to that of the fire spread rate model for grazed or cut grass fuel conditions as recommended for general use during the summer fire season in southern Australia.

Value of the Copernicus Arctic Regional Reanalysis (CARRA) in representing near-surface temperature and wind speed in the north-east European Arctic

The representation of 2-m air temperature and 10-m wind speed in the high-resolution (with a 2.5-km grid spacing) Copernicus Arctic Regional Reanalysis (CARRA) and the coarser resolution (ca. 31-km grid spacing) global European Center for Medium-range Weather Forecasts fifth-generation reanalysis (ERA5) for Svalbard, northern Norway, Sweden and Finland is evaluated against observations. The largest differences between the two reanalyses are found in regions with complex terrain and coastlines, and over the sea ice for temperature in winter. In most aspects, CARRA outperforms ERA5 in its agreement with the observations, but the value added by CARRA varies with region and season. Furthermore, the added value by CARRA is seen for both parameters but is more pronounced for temperature than wind speed. CARRA is in better agreement with observations in terms of general evaluation metrics like bias and standard deviation of the errors, is more similar to the observed spatial and temporal variability and better captures local extremes. A better representation of high-impact weather like polar lows, vessel icing and warm spells during winter is also demonstrated. Finally, it is shown that a substantial part of the difference between reanalyses and observations is due to representativeness issues, that is, sub-grid variability, which cannot be represented in gridded data. This representativeness error is larger in ERA5 than in CARRA, but the fraction of the total error is estimated to be similar in the two analyses for temperature but larger in ERA5 for wind speed.

Modeling the impacts of land use/land cover change on meteorology and air quality during 2000–2018 in the Yangtze River Delta region, China

The land use/land cover (LULC) change in the fast-developing city clusters of China exhibits impacts on both the meteorology and air quality. However, this effect, especially in the Yangtze River Delta (YRD), has not been well quantified. In this study, the LULC data are extracted from Landsat satellite imageries for year 2000 and 2018 for the YRD region. The Weather Research and Forecasting with Chemistry (WRF/Chem) model is applied to investigate the impact of historical LULC change on regional meteorology and air pollution over the YRD region during the past two decades. Two simulation scenarios are performed with two sets of LULC data to represent the pre-urbanization (LULC of year 2000) and the most recent urban pattern (LULC of year 2018). Results indicate that rapid urbanization leads to an increase of monthly mean 2-m temperature by 0.4–2.1 °C but decrease of the 10-m wind speed by 0.5–1.3 m/s in urban areas; the maximum increase of daytime planetary boundary layer height (PBLH) in July and November is 289 and 132 m, respectively. Affected by favorable changes in the meteorological conditions due to LULC change, the PM2.5 concentrations in most urban areas show a decreasing trend, especially during the nighttime in summer. On the contrary, surface ozone (O3) concentration in urban areas has increased by 7.2–9.8 ppb in summer and 1.9–2.1 ppb in winter. Changes in O3 concentration are inversely proportional to changes in NOx and the spatial distribution of PM2.5. Areas with higher O3 concentration are consistent with areas of higher temperature and lower wind speed. Our findings reveal that LULC changes during the past years bring observable changes in air pollutant concentrations, which should not be neglected in the YRD region regarding air quality trends as well as policy evaluations under the warming threat.

Evaluation of Marine Wind Profiles in the North Sea and Norwegian Sea Based on Measurements and Satellite-Derived Wind Products

The wind conditions at oil platforms are typically measured at 40–140-m height, and then reduced to standard 10-m height before being distributed via the Global Telecommunication System. The 10-m values are further used for assimilation and verification of weather forecasts models. An accurate representation of the wind profile is therefore essential. Here five wind profiles; power law, Norsok, logarithmic, Monin-Obukhov and Gryning are studied to find the best method for reduction of platform wind speeds to 10-m height level. Observations from nine oil platforms are used together with 10-m wind speed from ASCAT for evaluation of the wind profiles. In addition the wind profiles are evaluated using observations from the FINO3 offshore mast outside Denmark. The present wind profile used for wind reduction at the oil platforms (power law with a constant wind-shear coefficient of 0.13) underestimates the 10-m wind speed by as much as 0.8 m/s on average. For near neutral atmospheric stability the Norsok and the logarithmic wind profiles yield on average a near perfect fit with a wind shear coefficient of 0.085. However, 10-m wind speeds are underestimated for unstable and overestimated for stable conditions. The Monin-Obukhov and Gryning wind profiles give the most accurate estimates of 10-m wind speed during unstable and near-neutral conditions. For stable situations the Gryning wind profile, which also includes information about height of the boundary layer, gives the best agreement, but still an underestimation of the low level wind speed is encountered. The results strongly underline that atmospheric stability needs to be taken into account. For future wind reduction from observation level to 10 meter, it is recommended to use a method which takes stability into account, for example the Gryning method. For long-term average assessments however, the logarithmic and Norsok wind profiles are sufficient.

Quantifying Estuarine Hydrometeorological Coastal Hazards Using a Combined Field Observation and Modeling Approach

Coastal development and its associated site management have rapidly expanded to estuarine environments while continuing to increase worldwide. With the growth of coastal management projects, field observations are required to understand how anthropogenic construction, coastal defense, environmental restoration, and conservation areas will react to the typical, extreme, and long-term conditions at the proposed sites. To address these unknowns, we present a multi-faceted coastal risk assessment of a unique, recently nourished estuarine beach near the mouth of the Delaware Bay Estuary by merging rapid-response remote sensing platforms, hydrodynamic models, and publically available monitoring datasets. Specifically, hydrometeorological events from 2015 to 2019 were the focus of peak-over-threshold statistics, event type definition, and clustered event interval determination. The 95th percentile thresholds were determined to be the following: 0.84 m for the significant wave height, 13.5 m/s for the 10-m elevation wind speed, and 0.4 m for the total water level residuals. Tropical and extra-tropical cyclones, light gales, or cold and stationary fronts proved to be the meteorological causes of the sediment mobility, inducing the hydrodynamics at the site. Using these event types and exceedance instances, clustered meteorological events were defined as having an interval greater than twelve hours but less than five days to be considered clustered. Clustered events were observed to cause greater volumetric change than individual events, and are currently underrepresented in coastal risk planning and response in the region. Coastal monitoring field measurements should consider clustered events when conducting post-hazardous or erosional event response surveys. This work highlights the importance of clustered hydrometeorological events causing estuarine coastal risk, and how to quantify these effects through combined field observations and modeling approaches.

Improved Urban Finescale Forecasting During a Heat Wave by Using High-Resolution Urban Canopy Parameters

For urban weather finescale forecasting, obtaining accurate and up-to-date urban canopy parameters (UCPs) is necessary and still a challenge. In this study, a high-resolution dataset of UCPs was developed by using vector-format building information and then applied in the WRF/urban system with the single-layer urban canopy model (SLUCM)/building effect parameterization (BEP) model to improve the urban finescale forecasting of a typical heat wave event during summer 2016 in Hangzhou. A series of sensitivity experiments were conducted, and the results showed that the high-resolution UCP data improved the model skill in simulating the spatial distributions and diurnal variations of 2-m temperature, 2-m relative humidity, and 10-m wind speed in the urban areas of Hangzhou, especially for the BEP model. Better results were produced when refining the computation domain due to more realistic urban morphological characteristics were adopted. The sensitive experiments suggest that the high-resolution UCPs played a significant role in representing the UHI effect though changing the surface thermodynamic parameters (e.g., roughness length), hereafter increasing the sensible heat and surface heat flux, and finally resulting a notable urban heat island (UHI) effect.

A revisit of global wind-sea and swell climate and variability using multiplatform altimeters

A well-performed (root mean square error ≈ 0.35 m) look-up table model was established to separate the wind-sea and swell significant wave height (Hs) using 10-m sea surface wind speed (U10) and Hs data from altimeters. Based on this model, global wind-sea and swell climate and variability were investigated using 27-year jointly calibrated altimeter data. The climatology of the swell and wind-sea Hs (HWS and HSW) are in good agreement with those of the total Hs and U10, respectively. This is because of the dominance of swells in the World Ocean with respect to both occurrence frequency and energy proportion and the close coupling between wind and wind-seas. The interannual variability of HWS and HSW are closely related to some climate indices, but the responses of wind-seas and swells are different for the same atmospheric oscillation because of the large propagation distances of swells. For long-term variability, significant positive/negative long-term trends of HWS/HSW were found almost all over the World Ocean. Such opposite trends can be explained by the positive/negative trends of U10/Hs: a shorter fetch or duration of wind leads to a lower Hs, but a higher U10 leads to higher wind-sea probability and energy weight. However, large uncertainty still exists in these trends and further exploration is needed.

On Dropsonde Surface-Adjusted Winds and Their Use for the Stepped Frequency Microwave Radiometer Wind Speed Calibration

The airborne Stepped Frequency Microwave Radiometer (SFMR) provides measurements of 10-m ocean-surface wind speed in high and extreme wind conditions. These winds are calibrated using the surface-adjusted wind estimates from the so-called dropsondes. The surface-adjusted winds are obtained from layer-averaged winds scaled to 10-m altitude to eliminate the local surface variability not associated with the storm strength. The SFMR measurements and, consequently, the surface-adjusted dropsonde winds represent a possible reference for satellite instrument and model calibration/validation at high and extreme wind conditions. To this end, representativeness errors that those measurements may introduce need to be taken into account to ensure that the storm variability is correctly resolved in satellite retrievals and modelling. In this work, we compare the SFMR winds with the dropsonde surface-adjusted winds derived from the so-called WL150 algorithm, which uses the lowest 150-meter layer between 10 m to 350 m. We use nine years of data from 2009 to 2017. We focus on the effects of the layer altitude and thickness. Our analysis shows that the layer altitude has a significant impact on dropsonde/SFMR wind comparisons. Moreover, the averaged winds obtained from layers thinner than the nominal 150 m and closer to the surface are more representative of the SFMR surface wind speed than the WL150 speeds. We also find that the surface-adjusted winds are more representative of 10-km horizontally averaged SFMR winds. We conclude that for calibration/validation purposes, the WL150 algorithm can introduce noise and the use of actual 10-m dropsonde measurements should be further investigated.

Influences of Two-Scale Roughness Parameters on the Ocean Surface Emissivity From Satellite Passive Microwave Measurements

In this study, a method for estimating two-scale roughness influences on the ocean surface emissivity is developed by solving a simplified two-scale ocean emissivity model equation. In this model, scatterings by small-scale roughness are described by the Kirchhoff approximation. For large-scale roughness, the mean local incidence angle (LIA) is introduced to describe slanted surface slope deviation from flat surface. This study focuses on the ocean state under low/moderate wind conditions in order to preclude foam and anisotropic influences within the model. Consequently, a unique pair of two-scale roughness parameters are estimated from the equation using observed ocean emissivities from AMSR2-measured radiances. The results show that the estimated small-scale roughness at 6.925 and 10.65 GHz is linearly correlated with the 10-m height wind speed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U_{10}$ </tex-math></inline-formula> . As the frequency reaches 36.5 GHz, however, the scatters between small-scale roughness and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U_{10}$ </tex-math></inline-formula> are increased, which suggests that the Kirchhoff bistatic scattering function is not fully suitable to describe the small-scale roughness at this frequency. The linear relationships between mean LIA and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U_{10}$ </tex-math></inline-formula> are found with high correlation coefficients. In addition, the estimated mean LIA corresponds well with associated roughness calculated from both observed and modeled ocean wave height spectra. This evidence demonstrates that the proposed large-scale roughness parameterization is physically meaningful and, therefore, the mean LIA has a physical basis in large-scale roughness. In addition, the strong correlations between the roughness parameters and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U_{10}$ </tex-math></inline-formula> demonstrate the possibility to estimate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U_{10}$ </tex-math></inline-formula> from the AMSR2 data using intermediate parameters that are physically based on ocean surface characteristics.

Evaluation of Forecast Performance for Four Meteorological Models in Summer Over Northwestern China

At present, numerical models, which have been used for forecasting services in northwestern China, have not been extensively evaluated. We used national automatic ground station data from summer 2016 to test and assess the forecast performance of the high-resolution global European Centre for Medium-Range Weather Forecast (ECMWF) model, the mesoscale Northwestern Mesoscale Numerical Prediction System (NW-MNPS), the global China Meteorological Administration T639 model, and the mesoscale Global/Regional Assimilation and Prediction System (GRAPES) model over northwestern China. The root mean square error (RMSE) of the 2-m temperature forecast by ECMWF was the lowest, while that by T639 was the highest. The distribution of RMSE for each model forecast was similar to that of the difference between the modeled and observed terrain. The RMSE of the 10-m wind speed forecast was lower for the global ECMWF and T639 models and higher for the regional NW-MNPS and GRAPES models. The 24-h precipitation forecast was generally higher than observed for each model, with NW-MNPS having the highest score for light rain and heavy storm rain, ECMWF for medium and heavy rain, and T639 for storm rain. None of the models could forecast small-scale and high-intensity precipitation, but they could forecast large-scale precipitation. Overall, ECMWF had the best stability and smallest prediction errors, followed by NW-MNPS, T639, and GRAPES.

Application of Bias Correction to Improve WRF Ensemble Wind Speed Forecast

Surface wind speed forecast from an operational WRF Ensemble Prediction System (WEPS) was verified, and the system-bias representations of the WEPS were investigated. Results indicated that error characteristics of the ensemble 10-m wind speed forecast were diurnally variated and clustered with the usage of the planetary boundary layer (PBL) scheme. To correct the error characteristics of the ensemble wind speed forecast, three system-bias representations with decaying average algorithms were studied. One of the three system-bias representations is represented by the forecast error of the ensemble mean (BC01), and others are assembled from each PBC group (BC03) as well as an independent member (BC20). System bias was calculated daily and updated within a 5-month duration, and the verification was conducted in the last month, including 316 gauges around Taiwan. Results show that the mean of the calibrated ensemble (BC03) was significantly improved as the calibrated ensemble (BC20), but both demonstrated insufficient ensemble spread. However, the calibrated ensemble, BC01, with the best dispersion relation could be extracted as a more valuable deterministic forecast via the probability matched mean method (PMM).

Cool-roof effects on thermal and wind environments during heat waves: A case modeling study in Seoul, South Korea

This study examines cool-roof effects on thermal and wind environments in Seoul, South Korea during the 2018 heat wave through Weather Research and Forecasting (WRF) model simulations. Two 37-day simulations, one with cool roofs that have a higher albedo and the other with conventional roofs, are conducted. In the daytime, cool roofs decrease the 2-m temperature, 10-m wind speed, surface sensible and latent heat fluxes, and planetary boundary layer height but increase the 2-m water vapor mixing ratio. The daytime maximum decreases are 1.0 °C in 2-m temperature and 0.5 m s−1 in 10-m wind speed. The cool roof-induced reduction of near-surface temperature tends to decrease with increasing heat wave intensity. The decrease in near-surface temperature weakens convergence of near-surface wind, indicating the weakening of daytime urban breeze. Also, the decrease in near-surface temperature, thus decreased land-sea air temperature difference, leads to the weakening of sea breeze. Interestingly, there are occasions on which the cool-roof case produces higher near-surface temperature at late afternoon times in a region of Seoul than the conventional-roof case. This can occur when the cooling caused by the sea breeze in the conventional-roof case is dominant over the cooling due to cool roofs in the cool-roof case.

Filling the matrix: an ANOVA-based method to emulate regional climate model simulations for equally-weighted properties of ensembles of opportunity

Collections of large ensembles of regional climate model (RCM) downscaled climate data for particular regions and scenarios can be organized in a usually incomplete matrix consisting of GCM (global climate model) x RCM combinations. When simple ensemble averages are calculated, each GCM will effectively be weighted by the number of times it has been downscaled. In order to facilitate more equal and less arbitrary weighting among downscaled GCM results, we present a method to emulate the missing combinations in such a matrix, enabling equal weighting among participating GCMs and hence among regional consequences of large-scale climate change simulated by each GCM. This method is based on a traditional Analysis of Variance (ANOVA) approach. The method is applied and studied for fields of seasonal average temperature, precipitation and surface wind and for the 10-year return value of daily precipitation and of 10-m wind speed for a completely filled matrix consisting of 5 GCMs and 4 RCMs. We quantify the skill of the two averaging methods for different numbers of missing simulations and show that ensembles where lacking members have been emulated by the ANOVA technique are better at representing the full ensemble than corresponding simple ensemble averages, particularly in cases where only a few model combinations are absent. The technique breaks down when the number of missing simulations reaches the sum of the numbers of GCMs and RCMs. Also, the method is only useful when inter-simulation variability is limited. This is the case for the average fields that have been studied, but not for the extremes. We have developed analytical expressions for the degree of improvement obtained with the present method, which quantify this conclusion.

A Statistical Analysis of High-Frequency Track and Intensity Forecasts from NOAA’s Operational Hurricane Weather Research and Forecasting (HWRF) Modeling System

AbstractA statistical analysis is performed on the high-frequency (3⅓ s) output from NOAA’s cloud-permitting, high-resolution operational Hurricane Weather Research and Forecasting (HWRF) Model for all tropical cyclones (TCs) in the North Atlantic Ocean basin over a 3-yr period (2017–19). High-frequency HWRF forecasts of TC track and 10-m maximum wind speed (Vmax) exhibited large fluctuations that were not captured by traditional low-frequency (6 h) model output. Track fluctuations were inversely proportional to Vmax, with average values of 6–8 km. The Vmax fluctuations were as high as 20 kt (10.3 m s−1) in individual forecasts and were a function of maximum intensity, with a standard deviation of 5.5 kt (2.8 m s−1) for category-2 hurricanes and smaller fluctuations for tropical storms and major hurricanes. The radius of Vmax contracted or remained steady when TCs rapidly intensified in high-frequency HWRF forecasts, consistent with observations. Running-mean windows of 3–9 h were applied at synoptic times to smooth the high-frequency HWRF output to investigate its utility to operational forecasting. Smoothed high-frequency HWRF output improved Vmax forecast skill by up to 8% and produced a more realistic distribution of 6-h intensity change when compared with low-frequency, instantaneous output. Furthermore, the high-frequency track forecast output may be useful for investigating characteristics of TC trochoidal motions.

Impact of CYGNSS-Derived Winds on Tropical Cyclone Forecasts in a Global and Regional Model

AbstractAn observing system experiment was conducted to assess the impact of wind products derived from the Cyclone Global Navigation Satellite System (CYGNSS) on tropical cyclone track, maximum 10-m wind speed Vmax, and minimum sea level pressure forecasts. The experiment used a global data assimilation and forecast system, and the impact of both CYGNSS-derived scalar and vector wind retrievals was investigated. The CYGNSS-derived vector wind products were generated by optimally combining the scalar winds and a gridded a priori vector field. Additional tests investigated the impact of CYGNSS data on a regional model through the impact of lateral boundary and initial conditions from the global model during the developmental phase of Hurricane Michael (2018). In the global model, statistically significant track forecast improvements of 20–40 km were found in the first 60 h. The Vmax forecasts showed some significant degradations of ~2 kt at a few lead times, especially in the first 24 h. At most lead times, impacts were not statistically significant. Degradations in Vmax for Hurricane Michael in the global model were largely attributable to a failure of the CYGNSS-derived scalar wind test to produce rapid intensification in the forecast initialized at 0000 UTC 7 October. The storm in this test was notably less organized and symmetrical than in the control and CYGNSS-derived vector wind test. The regional model used initial and lateral boundary conditions from the global control and CYGNSS scalar wind tests. The regional forecasts showed large improvements in track, Vmax, and minimum sea level pressure.

A Kilometer-Scale Coupled Atmosphere-Wave Forecasting System for the European Arctic

AbstractAccurately simulating the interactions between the components of a coupled Earth modelling system (atmosphere, sea-ice, and wave) on a kilometer-scale resolution is a new challenge in operational numerical weather prediction. It is difficult due to the complexity of interactive mechanisms, the limited accuracy of model components and scarcity of observations available for assessing relevant coupled processes. This study presents a newly developed convective-scale atmosphere-wave coupled forecasting system for the European Arctic. The HARMONIE-AROME configuration of the ALADIN-HIRLAM numerical weather prediction system is coupled to the spectral wave model WAVEWATCH III using the OASIS3 model coupling toolkit. We analyze the impact of representing the kilometer-scale atmosphere-wave interactions through coupled and uncoupled forecasts on a model domain with 2.5 km spatial resolution. In order to assess the coupled model’s accuracy and uncertainties we compare 48-hour model forecasts against satellite observational products such as Advanced Scatterometer 10 m wind speed, and altimeter based significant wave height. The fully coupled atmosphere-wave model results closely match both satellite-based wind speed and significant wave height observations as well as surface pressure and wind speed measurements from selected coastal station observation sites. Furthermore, the coupled model contains smaller standard deviation of errors in both 10m wind speed and significant wave height parameters when compared to the uncoupled model forecasts. Atmosphere and wave coupling reduces the short term forecast error variability of 10 m wind speed and significant wave height with the greatest benefit occurring for high wind and wave conditions.