Comment on acp-2022-811

A preliminary geophysical model function, relating the sea surface brightness temperatures to ocean surface wind speed and direction, was developed using the data acquired at 45/spl deg/, 55/spl deg/, and 65/spl deg/ incidence angles by Jet Propulsion Laboratory's (JPL) aircraft 19- and 37-GHz polarimetric radiometers in 1994 and 1995. Radiometric temperatures from all polarization channels under cloud-free conditions showed clear dependence on surface wind direction. When there were stratus or scattered clouds, T/sub /spl nu// and T/sub h/ were significantly influenced by the radiation from cloud water, but the polarimetric channel U was found to be insensitive to clouds. The Fourier harmonic coefficients of the wind direction signals were derived from experimental data and related to the wind speed and direction, incidence angle and frequency. In general, all harmonic coefficients increase from low to moderate wind speeds, except the sin 2/spl phi/ component of U at 65/spl deg/ incidence, which peaked at low winds with a peak-to-peak amplitude of 0.6 to 1 Kelvin at about 3 m/s winds. At moderate wind speeds, 45/spl deg/ incidence angle exhibits larger second harmonic signals, but smaller first harmonic signals, than higher incidence angles. Wind direction signals were similar in 19 and 37 GHz channels, but the 37 GHz channel showed a slightly stronger wind direction sensitivity than the 19 GHz channel. The results suggest promising applications of passive microwave radiometers to ocean wind vector measurements.

Geomorphic and tree shelter in relation to surface wind conditions, weather, time of day and season

A method is proposed for extracting high-resolution surface wind speed and direction using the ERS-1 synthetic aperture radar imagery. The radar pixel intensities must be converted and calibrated into radar cross section. In addition, two-dimensional image structure directly related to the wind direction is required. The wind direction is needed to increase the accuracy of the wind speed estimates. The wind speed algorithm error is estimated at about /spl plusmn/1.5 to 2 m/s if the cross sectional variation with wind direction is ignored. >

<p>Arctic sea ice is steadily retreating due to climate warming, but regional and seasonal variations in Arctic sea ice are important. This study aims in understanding how the winter atmospheric circulation affects the sea ice drift and how ice motion contributes to regional sea ice concentration and thickness anomalies. Sea ice conditions in late winter and spring are crucial for predicting summer sea ice. Understanding the mechanism that affect the spring sea ice concentration and thickness have potential to improve sea ice predictions year-round.  </p><p>Atmospheric pressure patterns affect the thermodynamic vertical structure of the atmosphere, heat and moisture transport to the Arctic, and radiative and turbulent fluxes at the surface (eg. Nygård et al 2021). Circulation types also control surface wind speed and direction, and are closely linked to ice drift speed (eg. Mallett et al 2021). Winter atmospheric circulation can precondition spring sea ice anomalies and summer melt by ice dynamics (eg. sea ice transport to lower latitudes where it is more vulnerable to melt) and thermodynamics (eg. positive surface energy balance anomalies prohibiting ice growth in winter leading to thinner ice in spring).</p><p>In this study we present a Self Organizing Maps (SOM) clustering of the winter (December-March) mean sea level pressure to detect the typical circulation patterns. The SOM-analysis covers the period from December 2000 to March 2021. The circulation patterns, or SOM nodes, are linked to atmospheric conditions (surface energy balance and wind speed) and sea ice conditions (concentration, drift speed and thickness). We use data from ERA5, ORAS5, and PIOMAS reanalyses, and Polar Pathfinder Sea Ice Motion and Cryosat2-SMOS ice thickness remote sensing products. We show how the circulation types are linked to near surface wind speed and direction, and consequent sea ice drift.  As a result, we analyze if the circulation patterns can be linked to sea ice anomalies thought sea ice dynamics or thermodynamics.</p>

Abstract. The new scatterometer Advanced SCATterometer (ASCAT) onboard MetOp-A satellite provides surface wind speed and direction over global ocean with a spatial resolution of 25 km square over two swaths of 550 km widths. The accuracy of ASCAT wind retrievals is determined through various comparisons with moored buoys. The comparisons indicate that the remotely sensed wind speeds and directions agree well with buoy data. The root-mean-squared differences of the wind speed and direction are less than 1.72 m/s and 18°, respectively. At global scale, ASCAT winds are compared with surface winds derived from QuikSCAT scatterometer. The results confirm the buoy analyses, especially for wind speed ranging between 3 m/s and 20 m/s. For higher wind conditions, ASCAT is biased low. The ASCAT underestimation with respect to QuikSCAT winds is wind speed dependent. The comparisons based on the collocated scatterometer data collected after 17 of October 2007 indicate that there are significant improvements compared to previous periods.

Radar backscatter from the ocean depends on surface wind stress for a wide range of radar wavelengths. Here, we have reviewed the wind‐radar relationships for L band radar wavelengths near 25 cm and 20° angle of incidence and HH polarization using a number of aircraft and SEASAT‐I SAR observations. At this wavelength, the L band backscatter coefficient (σ°) from the ocean appears to depend upon surface wind speed (|U|) and direction (ϕ) in the following manner: σ° = K|Ū|a[1 + b cos (2ϕ)]. The wind‐speed coefficient a is 0.5±0.1 for a wide range of wind speeds. The wind‐direction coefficient b is near zero (i.e., b = 0.05 ± 0.05) for lower winds and stable marine boundary layers. However, one unusual aircraft observation suggests that b = 0.20 ± 0.05 for moderate wind speeds and an unstable marine boundary layer. We have examined a variety of aircraft and SEASAT‐I observations. SEASAT synthetic aperture radar (SAR) observations in conjunction with simultaneous SEASAT scatterometer measurements provide an excellent measurement of the wind‐speed coefficient a since a variety of wind speeds can be observed in a few minutes. Aircraft observations provide an excellent measurement of the wind‐direction coefficient b since a number of directions can be examined in about 1 hour with special flight patterns designed for multiple look directions. Thus, both aircraft and spacecraft observations were studied in this work. We have interpreted these aircraft and spacecraft results in terms of existing theoretical models for radar scattering from the ocean. We believe that modulation of echo power by the magnitude of the wind occurs through both large‐scale slope effects as well as through growth of the short gravity wave‐height spectrum. Our data is near an angle of incidence of 20° where a two scale scattering model must be used.

Aircraft observations were performed over the sea near the southwest islands of Japan under Asian Atmospheric Particulate Environmental Change Experiment 2/Asian Pacific Regional Aerosol Characterization Experiment (APEX‐E2/ACE‐Asia) project during the period of 16–28 April 2001. The polluted air mass from east Asia was associated with very high concentrations of SO2 (1–10 ppb) and aerosol particles (3000–5000 cm−3) in the marine boundary layer. The cloud condensation nuclei (CCN) concentration at 0.3% supersaturation was as high as 800–2000 cm−3 during the penetrations of air pollutants from east Asia. The correlation coefficient between SO2 and aerosol particles was significant in such polluted atmosphere. Concentration of CCN (NCCN) was linearly related to concentration of aerosol particles (NAP) according to NCCN ∼ 0.75NAP. The ratio of CCN to aerosol condensation nuclei particle concentrations was lower than 0.3 in the relatively clean maritime atmosphere, but it was as high as ∼0.5 in the continentally influenced atmosphere in the boundary layer. These results indicated that the influence of anthropogenic pollutants from east Asia increased the contribution percentage of aerosol particles to CCN in the polluted atmosphere over the observation area. The observational results also indicated that a mean cloud droplet concentration (NC) in the continentally influenced clouds was ∼2 times as much as NC in the relatively clean maritime clouds. The slope of log‐log relationship between NC and NCCN was ∼0.39. This study strongly suggests that high CCN concentration formed many cloud droplets and decreased their effective radius at similar liquid water content under the outflow of air pollutants from east Asia.

The Special Sensor Microwave Imagers (SSM/I) flying on the DMSP polar orbiting derive relatively high resolution surface wind speeds over the oceans. The SSM/I is limited to ocean surface wind speed only. Recently, techniques to infer wind direction from passive microwave measurements have been developed. Polarimetric measurements are currently of interest. Active devices produce very high quality vector wind measurements. It is the goal of this paper to discuss a simple technique that could be used in conjunction with polarimetry and scatterometry to augment the complete description of boundary layer winds. Th authors discuss a simple technique first developed by T.S. Yu (1987), that yields a closed form solution to the horizontal momentum equations for the boundary layer vector wind field using in situ satellite measurements as a source of wind speed data, and surface pressure gradient data from state-of-the-art numerical weather prediction models. The SEASAT scatterometer and altimeter data were originally used as a proof of concept for this technique. The authors describe an implementation using the SSM/I as the source of wind speed data, and the Air Force Global Weather Central (AFGWC) High Resolution Analysis System (HIRAS) as the source of boundary layer pressure analysis. They calculate a wind field at the resolution of the SSM/I, with time and space interpolation of the model surface pressure analysis to match the satellite measurements. It should be noted that the HIRAS resolution is much coarser than the SSM/I data, therefore they are relying on the general smoothness ofthe surface pressure during the interpolation process.

Theoretically, in the natural condition the wind is main energy induce wave and sea surface current. The measurements related to the wind patterns may indirectly reflect the condition of waves and the sea surface current. Other theory stated that in some cases, sea surface current has different direction depending on the bathimetry condition or other local factors. This research aimed to discover the correlation between wind and sea surface current direction in Kendal Coastal Area, focussing on nearshore zone nearby Bodri Rivermouth. The research was conducted by a simple measuring instrument for detecting sea surface current direction. The background of this research was the occurrences of the lost people drowned in the research area and being related to the mystical phenomena by the coastal community. The results showed that the correlation values of the wind and sea surface direction up to 0.40 and 0.09, each for point 1 and point 2, respectively. Those differences between wind and sea surface current direction imply that the results against the first wind direction theory. However, the results revealed that the existence of river mouth is the main factor that influences the different pattern of the sea surface current and wind direction. It is proof that the outlet current exists in the research area.

ARMA based approaches for forecasting the tuple of wind speed and direction

Aerosol size distribution and new particle formation events in the suburb of Xi'an, northwest China

Studies of wind field characteristics have increased as wind affects heat and moisture transfer between the Earth’s surface and the atmosphere (Oke, 1987), particulate transports (Green et al., 1982; Cabezudo et al. 1997; Weber, 1998) and engineering design and construction (Cook, 1985; Liu, 1991). Wind speed is important for evaluating wind power potential (Brown et al., 1984; Palutikof et al., 1987; Rohatgi and Nelson, 1994), and so has become the research focus of wind power resources. Relatively few studies have been conducted on the wind regime across Hong Kong. Chen (1975) investigated and compared surface wind characteristics at 13 stations. Tam (1987) described the general wind pattern and the extreme winds caused by tropical cyclones. Lui (1991) compared wind data from automatic weather stations. Jeary (1997) studied wind in relation to the code of practice on wind effects. The most recent assessments of the wind regime were associated with wind power potential. Li (2000) examined the potential and feasibility of offshore wind energy on Waglan Island, Hong Kong. Lu and Yang (2001) analysed wind speed data for five sites. Lu et al. (2002) proposed a simulation model to assess the potential for wind power generation at Waglan Island. Wong and Kwan (2002) investigated the variations in wind energy resources over Hong Kong. This study employs scalar, directional and vector statistics (Klink, 1998, 1999) to examine the surface wind characteristics in Hong Kong. The objectives are to describe and evaluate seasonal means and interannual variability of surface wind speed, direction and velocity. Findings of this study have created a climatology of wind field and provide valuable information in relation to wind power potential, design and construction of buildings and particulate transport process. Methods and data

Wind speed has been retrieved reliably from SSM/I microwave radiometric measurements since 1990 with a precision of better than 2 m/s. However, operational requirements of the National Polar-orbiting Operational Environmental Satellite System (NPOESS) dictate retrieval of the ocean surface wind vector, i.e. both speed and direction. This requires improved understanding and forward modeling of the physical processes governing ocean surface microwave emissivity. Although the microwave brightness temperature over the ocean exhibits a relatively strong dependence on wind speed, the dependence on wind direction with respect to the azimuthal angle of observation is at most a few Kelvin peak-to-peak. Although it is known that breaking waves and foam significantly affect the microwave emission of the ocean surface at 6.8, 10.8, 19 and 37 GHz, the effect of foam on the azimuthal dependence of sea surface emissivity is not well understood. This understanding is critical to improve forward models to achieve the accuracy required for ocean surface wind vector retrievals. Recently, electromagnetic modeling of the emissivity of foam generated by breaking waves has been improved by considering the vertical and horizontal heterogeneity of thick layers of foam at the ocean- atmosphere interface [1]. In this work, we compare foam emissivities both calculated using this model with those inferred from microwave brightness temperature measurements performed during the Polarimetric Observations of the Emissivity of Whitecaps Experiment (POEWEX'04).

StreamLine XR Doppler LiDAR measurements during the four months of January-April 2022 provided first time high resolution temporal and spatial measurements of the boundary layer wind profile variability over Sde-Boker, located in the central Negev desert. This work presents characterization and comparison of the daily low tropospheric wind profile according to measurements and simulations with the new regional ICON-LAM (ICOsahedral Nonhydrostatic weather model in a Limited Area Mode) model with a convection-permitting resolution of ~2.5 km. Two main regimes of daily wind profile are observed: “Regular days”, with no sharp wind direction change. Most of these days present westerly component flow in the boundary layer (BL) up to 500-1000 m a.g.l, and typical daily variability of the boundary layer height (BLH) according to the solar heating with or without wind direction shear above the BL. Such events are mostly under high pressure from the West. Winter lows present strong (> 5 m/s) westerly flow with constant BLH. “Transitional days” presenting sharp wind direction change in the BL (at least 90° within an hour). Their frequency is ~ 30% during February-April, while during January single event occurred. The synoptic conditions present pronounced change in the synoptic gradients or mild synoptic gradients allowing the development of local mountain breeze. Mild gradients may occur under winter highs, Red Sea trough approaching winter lows and Sharav lows. Case studies of each group and their synoptic pressure gradients at the lowest troposphere (1000-500 hPa) are presented. The comparison between the model and measurements has temporal and vertical spatial resolutions of 1-hour and 100 m accordingly. Two sets of predictions from 12 UTC and 0 UTC initializations are separately applied. The absolute differences between the predicted and measured wind direction and speed are mostly up to 40 °and 3 m/s during the case studies. The predicted sharp wind direction transition times of the "transitional days" cases are 1-2 hours earlier than their measured counterparts. Due to this mismatch, large differences between the predicted and measured wind directions are observed (~ 100°). The 12 UTC initialization better predicts the transition times. January-April monthly absolute mean errors (AME) of wind direction and wind speed are 7°– 40 ° and 0 – 3.6 m/s (events with speed > 1 m/s). The biases are mostly -15 °– 22 ° and -1 – 1 m/s. The model nicely reconstructs the variability of the wind profile.

Titan’s climate includes dynamic processes at multiple scales, ranging from local turbulence to global super-rotation. Several mesoscale models are currently being developed which will permit simulations of surface winds at kilometric resolutions. However, mesoscale modeling requires high-resolution topography as an input, as it can be one of the primary controls on surface wind directions and amplitudes. The Cassini-Huygens mission, which spent 13 years in orbit around Saturn, mapped Titan’s surface at resolutions up to about 300 m/pixels with its RADAR instrument, which operated at a wavelength of 2.17 cm (13.78 GHz frequency, Ku-band). Titan’s best topography maps (Corlies et al., 2017) consist in a combination of radar altimetry, the SARTopo dataset (Stiles et al., 2009), and SAR stereoradargrammetry, interpolated where data is unavailable. The available topographic information thus has low spatial resolution, unequal coverage, and very variable accuracy (e.g., Fig. 1), rendering it unusable as an input for mesoscale modeling. In the absence of better topography data for the foreseeable future, we create fully parametrized synthetic topography, based on the surface geomorphology and the available topography. A similar method is used to create synthetic maps of the surface albedo, aerodynamic roughness length, and thermal inertia, whose influence on surface winds can thus be tested.In order to study (1) the surface atmospheric and sedimentary environment at the Dragonfly landing site and (2) the role of surface liquids in mesoscale atmospheric circulation, we focus on two regions: a 1000-km-wide region centered on the Dragonfly landing site near Selk crater, and a 250-km-wide area in the northern small lakes region. We first create a geomorphological map based on Cassini Synthetic Aperture Radar (SAR) images (Fig. 1), simpler than previous geomorphological maps of these regions (Malaska et al., 2016; Birch et al., 2017) in order to minimize the number of parameters. Each geomorphological unit (dune fields, mountains, plains, craters, lakes, valleys…) is given a fully parametrized shape, and a random surface topographic roughness is added everywhere (except on the smooth lakes). Reasonable values of the parameters are chosen from existing topography data, resulting in maps such as the one in Fig. 2. The same method is applied to create synthetic maps of the thermal inertia, surface albedo, and aerodynamic roughness z0: each unit is assigned a value chosen from the literature.The resulting topography, albedo, thermal inertia, and roughness maps are simplified and assume that all features of a unit are identical: these maps therefore look reasonable, but are non-unique and cannot match well with the observations. However, they have the great advantage of being parametrized, which allows us to vary each parameter and test its influence on mesoscale model outputs such as surface winds. Some key questions that we are seeking to answer include: At what height do crater rims and mountains on Titan start to significantly influence surface winds? How do the winds in a realistic lakes region compare to those over an idealized circular lake? Can topography cause wind patterns responsible for the dune morphologies observed on Titan, including cross-hatched dunes at the Dragonfly landing site?In the equatorial region, which is a dry, desert environment, we use the mesoscale model developed by Lefèvre et al. (abstract in this session), which couples the Weather-Research Forecast (WRF) nonhydrostatic dynamical core (Skamarock et al., 2008) with the LMD Titan PCM physics package (Lebonnois et al., 2012). Meanwhile, in the North polar small lakes region, we use the mesoscale Titan WRF, which reproduces the methane cycle, and can therefore model the evaporation and transport of methane (Chatain et al., abstract in this session).While other presentations at EPSC (Chatain et al., Lefèvre et al.) will focus on the mesoscale modeling efforts, including the effect of topography on the model, we will detail the synthetic topography model and present preliminary results on the relationship between surface winds and dune morphology for a “best guess” topography. In particular, we use the surface winds output by the mesoscale model in the equatorial regions as inputs to the IPGP dune morphodynamics model (Narteau et al., 2009) to attempt to reproduce the reticulated dunes observed around Selk crater (Malaska et al., 2016). Figure 1: Mapping the Selk crater region from Cassini SAR and topographyFigure 2: A synthetic topography map of the Selk crater region. Altitude is given in meters.