Coastal currents from Jason-2 and SARAL/AltiKa in the Indian region

In this study, data from space-based altimeters (Jason-2 and Satellite for ARgos and AltiKa [SARAL/AltiKa]) have been used to compute alongshore geostrophic currents in the coastal regions of the Indian mainland. These derived currents are compared with high-frequency (HF) radar observations. Beyond 30–40 km away from the coastline, altimeter-derived currents match fairly well with the HF radar data. Root mean square error (RMSE) of Jason-2-derived currents ranges between 0.3 and 0.6 m s^?1 while the same in the case of SARAL/AltiKa lies between 0.3 and 0.7 m s^?1. Satellite-derived across-track geostrophic current components (alongshore current) were also used to study the spatiotemporal variations of the east India coastal current (EICC). The coastal trapping of the EICC, its annual and intra-seasonal peaks are clearly observed in the power spectrum of time-series of Jason-2 and SARAL/AltiKa derived currents.     

Lidar profiling of aerosol scavenging parameters at a tropical station,Pune, India

In this study, we deal with observations of aerosol column content (height integration of vertical distribution of aerosol number density) that have been carried out using an Ar⁺ lidar for three different measurement cycles (each cycle consisting of three experimental days associated with non-rain, rain, and non-rain, respectively) of weekly spaced observations for pre-monsoon (March/April 1994), monsoon (September 1991), and post-monsoon (October 1998). Based on these observed profiles of aerosol number concentration on rainy days with respect to those on non-rainy days, vertical distributions of scavenging collection efficiencies (SCEs) are computed and discussed in this article. The SCE is found to decrease from 0.3 to 0.01 between the heights, 100 and 800 m for thunderstorm rain in April 1994, and during monsoon, it increases from 0.1 to 0.7. In the October 1998 episode, SCE was found to increase initially from 0.35 to 0.75 for heights between 40 and 200 m and thereafter decrease to 0.35 in the height interval of 200–800 m. For the rainfall intensity increase from 1 to 10 mm hour^?1, the corresponding scavenging coefficient (SC) for atmospheric layer 50–100 m varies from 4 × 10^?6 to 4 × 10^?5 s^?1 for thunderstorm in April 1994 and between 5 × 10^?6 and 5 × 10^?5 s^?1 in October 1998, respectively. During monsoon, these values vary from 3 × 10^?5 to 5 × 10^?4 s^?1. They lie in the range of those observed in the earlier field studies. The results are found useful to establish links between aerosols and cloud properties, and the influence of such interactions on weather and climate.     

Inter-comparison of wind profiles in the tropical boundary layer remotely sensed from GPS radiosonde and Doppler wind lidar

Winds play a very important role in the dynamics of the lower atmosphere, and there is a need to obtain vertical distribution of winds at high spatio-temporal resolution for various observational and modelling applications. Profiles of wind speed and direction obtained at two tropical Indian stations using a Doppler wind lidar during the Indian southwest monsoon season were inter-compared with those obtained simultaneously from GPS upper-air sounding (radiosonde). Mean wind speeds at Mahbubnagar (16.73° N, 77.98° E, 445 m above mean sea level) compare well in magnitude for the entire height range from 100 m to 2000 m. The mean difference in wind speed between the two techniques ranged from ?0.81 m s^?1 to +0.41 m s^?1, and the standard deviation of wind speed differences ranged between 1.03 m s^?1 and 1.95 m s^?1. Wind direction by both techniques compared well up to about 1200 m height and then deviated slightly from each other at heights above, with a standard deviation in difference of 19°–48°. At Pune (18^○32′ N, 73^○51′ E, 559 m above mean sea level), wind speed by both techniques matched well throughout the altitude range, but with a constant difference of about 1 m s^?1. The root mean square deviation in wind speed ranged from 1.0 to 1.6 m s^?1 and that in wind direction from 20° to 45°. The bias and spread in both wind speed and direction for the two stations were computed and are discussed. The study shows that the inter-comparison of wind profiles obtained by the two independent techniques is very good under conditions of low wind speeds, and they show larger deviation when wind speeds are large, probably due the drift of the radiosonde balloon away from the location.     

Ecosystem assessment in the Tonle Sap Lake region of Cambodia using RADARSAT-2 Wide Fine-mode SAR data

The Tonle Sap Lake (TSL), located in Cambodia, is the largest freshwater lake in Southeast Asia and has significant ecological, economic, and sociocultural value. The TSL’s ecosystems have been affected by climate change and an increasing amount of human activity in recent years. Considering that the TSL area is often covered by clouds, particularly in the rainy season, synthetic aperture radar (SAR) data are suitable for assessing the ecosystems in this great lake, as SAR enables weather- and cloud-independent observations. In this study, we investigated the capability of the RADARSAT-2 Wide Fine (WF) mode dual-polarization SAR data with a scene size of 170 × 150 km (azimuth × range) and a resolution of 7.6 × 5.2 m to study TSL’s ecosystem, by analysing the usefulness of backscattering coefficients and scattering mechanism-related parameters in identifying artificial targets and different land-cover types. The results of this study demonstrate the applicability of RADARSAT-2 WF-mode SAR data in the study of TSL’s ecosystems.     

Whole body vibration exposure patterns in Canadian prairie farmers

Abstract

Whole body vibration is a significant physical risk factor associated with low back pain. This study assessed farmers’ exposure to whole body vibration on the Canadian prairies according to ISO 2631-1. Eighty-seven vibration measurements were collected with a triaxial accelerometer embedded in a rubber seat pad at the operator-seat interface of agricultural machinery, including tractors, combines, pickup trucks, grain trucks, sprayers, swathers, all-terrain vehicles, and skid steers. Whole body vibration was highest in the vertical axis, with a mean (range) frequency-weighted root mean squared acceleration of 0.43 m/s² (0.19?1.06 m/s²). Mean crest factors exceeded 9 in all 3 axes, indicating high mechanical shock content. The vertical axis vibration dose value was 7.55 m/s^1.75 (2.18?37.59 m/s^1.75), with 41.4% of measurements within or above the health guidance caution zone. These high exposures in addition to an ageing agricultural workforce may increase health risks even further, particularly for the low back.

Practitioner Summary: Agricultural workers are frequently exposed to whole body vibration while operating farm equipment, presenting a substantial risk to musculoskeletal health including the low back. Assessing vibration exposure is critical in promoting a safe occupational environment, and may inform interventions to reduce farmer’s exposure to vibration.     

A new wind retrieval algorithm for Jason-1 at high wind speeds

The altimeter wind speed algorithm at high wind speeds remains unsolved because of lack of observed data. In this study data at high wind speeds were generated using Yin's typhoon model, which consists of the Rankine vortex model and the angular momentum model with typhoon parameters, provided by the Joint Typhoon Warning Centre (JTWC). The accuracy of Yin's typhoon model can be validated by comparing it with recorded data from a weather station. By comparing the normalized radar backscatter cross‐section (NRCS) detected by the Jason‐1 altimeter with wind speed data inferred by Yin's typhoon model, an empirical algorithm valid for a range of wind speeds between 10 and 40 m s^–1 is developed and proposed. The proposed algorithm is compared with the Jason‐1 operational algorithm and Young's altimeter wind retrieval algorithm. The study shows that, for the proposed algorithm and the operational algorithm for Jason‐1, the root mean square (RMS) errors are 3.38 and 3.60 m s^–1, respectively, and the average relative errors are 18% and 19%, respectively, for wind speeds less than 27 m s^–1. Hence, the proposed algorithm is in agreement with the operational algorithm for the Jason‐1 altimeter for wind speeds in the range 10–27 m s^–1. However, the Jason‐1 operational algorithm is inaccurate for wind speeds above 27 m s^–1 because the wind speeds used in the algorithm training process came from scatterometer wind products, and are significantly lower than those in strong wind and heavy rain conditions. Comparison of the proposed algorithm and Young's algorithm shows that the RMS errors are 6.27 and 15.18 m s^–1, respectively, and the average relative errors are 16% and 59%, respectively, for wind speeds greater than 20 m s^–1. The Holland typhoon model cannot accurately determine the outer wind field of typhoons since it extends cyclonic wind speeds to infinity. Young's altimeter wind retrieval algorithm depends on the Holland typhoon model, and the latter results in some errors. Compared with Young's altimeter wind retrieval algorithm, the proposed algorithm retrieves wind speeds with better accuracy. Therefore, the proposed algorithm, suitable for retrieving sea surface wind speeds in typhoons and other strong wind conditions, can be considered as supplementary to the Jason‐1 operational algorithm.     

Whole-body vibration exposure in sport: four relevant cases

This study investigates the whole-body vibration exposure in kite surfing, alpine skiing, snowboarding and cycling. The vibration exposure was experimentally evaluated following the ISO 2631 guidelines. Results evidenced that the most critical axis is the vertical one. The weighted vibration levels are always larger than 2.5 m/s² and the vibration dose values are larger than 25 m/s^1.75. The exposure limit values of the EU directive are reached after 8–37 min depending on the sport. The vibration magnitude is influenced by the athletes’ speed, by their skill level and sometimes by the equipment. The large vibration values suggest that the practice of sport activities may be a confounding factor in the aetiology of vibration-related diseases.     

Ventral-aspect radar cross sections and polarization patterns of insects at X band and their relation to size and form

A data set of ventral-aspect insect radar cross-sections (RCSs) and polarization patterns, measured at X band (9.4 GHz, linear polarization) in laboratory rigs, has been collated from a number of sources. The data have been analysed to identify relationships between RCS parameters (one representing size and two the polarization-pattern shape) and the insects’ masses and morphological dimensions and forms. An improved mass-estimation relationship, with appropriate asymptotes for very small and very large insects, is presented. This relationship draws only on the RCS size parameter and it is shown that incorporating one or both of the RCS shape parameters provides little additional benefit. Small insects have polarization-pattern shapes that fall within a relatively limited region of the range of parameter values allowed by electromagnetic scattering theory. Larger insects have shapes that extend beyond this region, following a broad trajectory as size and mass increases; at masses above ~0.6 g, the pattern becomes ‘perpendicular’, with maxima when the E-field is orthogonal to the body axis rather than parallel to it. RCS shape can be used to infer morphological form for small insects (<80 mg), but not for larger ones. These results are consistent with observations from X-band vertical-beam entomological radars and provide a basis for identification, at least to broad taxon classes, of the targets detected by such radars.     

Measurement of the left‐lateral displacement of Ms 8.1 Kunlun earthquake on 14 November 2001 using Landsat‐7 ETM+ imagery

An imageodesy study has been carried out, using pre‐ and post‐event Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) images, to reveal regional co‐seismic displacement caused by the Ms 8.1 Kunlun earthquake in November 2001. The two Landsat scenes, Kusai Lake and Buka Daban, cover an area of some 57 600 km² (320 km W–E and about 180 km N–S), which includes most of the fault rupture zone. The co‐seismic displacement measured in the Kusai Lake scene shows that the average left‐lateral shift along the Kunlun fault is 4.8 m (ranging from 1.5 to 8.1 m) and the maximum shift appears west of the Kusai Lake. The splayed nature of the fault to the west of Buka Daban, where the fault splits into three branches, causes the displacement pattern to become complicated. Here the average left‐lateral shift, between the south side of the southern branch and the north side of the northern branch, is 4.6 m (ranging from 1.0 to 8.2 m). Our results also illustrate that the south side of the fault is the ‘active’ block, moving significantly in an east–south‐easterly direction, relative to the largely ‘stable’ northern block.     

Assessment of multispectral ATSR2 stereo cloud-top height retrievals

The Along-Track Scanning Radiometer 2 (ATSR2) instrument has a dual view capability that allows for stereo height retrievals. Stereo heights were retrieved for selected scenes over the United Kingdom Chilbolton Facility for Atmospheric and Radio Research and the United States Atmospheric Radiation Measurement program Southern Great Plains site from 1997 to 2000. Stereo cloud-top heights obtained with the 11 μm and 1.6 μm channels of ATSR2 were compared with ground-based millimeter-wave cloud radar measurements at both sites. On average ATSR2 11 μm channel cloud-top height retrievals were 350 m higher than those from radar with a standard deviation of 1 km. This difference increased with decreasing cloud-top height. One major problem found in the 11 μm channel cloud-top height retrievals was poor delineation between surface (i.e., clear) and low-altitude cloud pixels, though this tends to lower cloud-top heights rather than raise them. The ATSR2 1.6 μm channel stereo cloud-top heights had large discrepancies compared to radar because of the 1.6 μm channel sensitivity to a lower layer in the case of multilayer clouds or the terrain in the case of optically thin single-layer clouds. For single-layer clouds, the agreement between the ATSR2 1.6 μm channel and the radar cloud-top heights was similar to that between ATSR2 11 μm channel and radar cloud-top heights.     

Upper ocean response to Super Typhoon Tembin (2012) explored using multiplatform satellites and Argo float observations

Using multiplatform satellites and in situ Argo float observations, this study systematically examined the upper ocean response to Super Typhoon Tembin (2012) in the western north pacific, and the interaction between typhoon and a pre-existing cold core eddy (CCE) was particularly focused on. Significant sea surface temperature (SST) cooling and sea surface height anomaly (SSHA) decrease was detected along track after typhoon, with the maximum SST cooling and SSHA decrease reaching 4.0°C and 25 cm, respectively. The pre-existing CCE was located to the left of the typhoon track, resulting in an intriguing leftward bias of SST cooling. The maximum SST cooling appeared at about 25 km to the left of the typhoon track, with SST cooling to the left of the track 40–100% larger than that to the right. After typhoon, the CCE was expanded by 50% due to the typhoon’s cyclonic wind stress. The thermocline was uplifted by 15–25 m by the typhoon-induced upwelling. Typhoon-enhanced vertical mixing was inferred from high-resolution Argo float data based on the Gregg–Henyey–Polzin parameterization method. The diapycnal diffusivity reached 9 × 10^?4 m² s^?1 after typhoon, which was more than 10 times larger than that before typhoon.     

An assessment of satellite-based agricultural water productivity over the Indian region

The preliminary analysis of agricultural water productivity (AWP) over India using satellite data were investigated through productivity mapping, water use (actual evapotranspiration (ET_a)/effective rainfall (R_eff) mapping and water productivity mapping. Moderate Resolution Imaging Spectroradiometer data was used for generating agricultural land cover (MCD12Q1 at 500 m), gross primary productivity (GPP; MOD17A2 at 1 km), and ET_a (MOD16A2 at 1 km). R_eff was estimated at 10 km using the United States Department of Agriculture soil conservation service method from daily National Oceanic and Atmospheric Administration Climate Prediction Center rainfall data. Six years’ (2007–2012) data were analysed from June to October. The seasonal AWP and rainwater productivity (RWP) were estimated using the ratios of seasonal GPP (kg C m^?2) and water use (mm) maps. The average AWP and RWP ranges from 1.10–1.30 kg Cm^?3 and 0.94–1.0 kg C m^?3, respectively, with no significant annual variability but a wide spatial variability over India. The highest AWP was observed in northern India (1.22–1.80 kg C m^?3) and lowest in western India (0.81–1.0 kg C m^?3). Large variations in AWP (0.69–1.80 kg C m^?3) were observed in Himachal Pradesh, Jammu and Kashmir, northeastern states (except Assam), Kerala, and Uttaranchal. The low GPP of these areas (0.0013–0.13 kg C m^?2) with low seasonal total ET_a (<101 mm) and R_eff (<72 mm) making the AWP high that do not correspond to high productivity but possible water stress. Gujarat, Rajasthan, Maharashtra, Madhya Pradesh, Jharkhand, and Karnataka showed low AWP (0.73–1.13 kg C m^?3) despite having high ET_a (261–558 mm) and high R_eff (287–469 mm), indicating significant scope for improving productivity. The highest RWP was observed in northern parts and Indo-Gangetic plains (0.80–1.6 kg C m^?3). The 6 years’ analysis reveals the status of AWP, leading to appropriate interventions to better manage land and water resources, which have great importance in global food security analysis.     

Symbolic target detection in SAR imagery via rotationally invariant-weighted feature extraction

This article introduces the application of a physics-based symbolic image partitioning method to detect targets in synthetic aperture radar (SAR) imagery. ‘Targets’ in this case refer to vehicular objects which produce a distinct radar return pattern, and have spatial characteristics that are known a priori. The proposed Rotationally Invariant Symbolic Histogram (RISH) detection method co-analyses both target and speckle statistics, and significantly reduces computational requirements by partitioning the data into a discrete number of state representations. RISH requires only one pass for robust detection, unlike other SAR detection methods which rely on difference metrics calculated using multiple passes. To improve performance in high-resolution data, RISH uses a weighted feature extraction algorithm to avoid the common requirement of processing each pixel of the image equally. The weighted structure extracts geometrically undefined and rotationally invariant target features. This article details the analysis of 24 experimentally obtained very high-frequency (VHF)-band SAR magnitude images using this novel approach to SAR target detection. In localizing small (~8.4 m²) foliage-concealed targets, without the aid of pre-processing, this method results in high performance characteristics (90% true positive) with a low Type-II error rate of 6.4 false alarms per 1 × 10⁶ m². With the addition of change detection, RISH lowers the error rate by 85%.     

SAR-derived wind fields at the coastal region in the East/Japan Sea and relation to coastal upwelling

The relationship between the modification of synthetic aperture radar (SAR) wind field and coastal upwelling was investigated using high-resolution wind fields from Advanced Land Observing Satellite (ALOS) Phased Array type L-band synthetic aperture radar (PALSAR) imagery and sea-surface temperature (SST) from National Oceanic and Atmospheric Administration/Advanced Very-High-Resolution Radiometer (NOAA/AVHRR) data. The retrieved SAR wind speeds seem to agree well with in situ buoy measurements with only a relatively small error of 0.7 m s^?1. The SAR wind fields retrieved from the east coast of Korea in August 2007 revealed a spatial distinction between near and offshore regions. Low wind speeds of less than 3 m s^?1 were associated with cold water regions with dominant coastal upwelling. Time series of in situ measurements of both wind speed and water temperature indicated that the upwelling was induced by the wind field. The low wind field from SAR was mainly induced by changes in atmospheric stability due to air–sea temperature differences. In addition, wind speed magnitude showed a positive correlation with the difference between SST and air temperature (R² = 0.63). The dependence of viscosity of water on radar backscattering at the present upwelling region was negligible since SAR data showed a relatively large backscattering attenuation to an SST ratio of 1.2 dB °C^?1. This study also addressed the important role of coastal upwelling on biological bloom under oligotrophic environments during summer.     

An algorithm for the retrieval of sea surface wind fields using X-band TerraSAR-X data

TerraSAR-X (TS-X) is a new, fully polarized X-band synthetic aperture radar (SAR) satellite, which is a successor of the Spaceborne Imaging Radar X-band Synthetic Aperture Radar (SIR-X-SAR) and the SRTM. TS-X has provided high-quality image products over land and oceans for scientific and commercial users since its launch in June 2007. In this article, a new geophysical model function (GMF) is presented to retrieve sea surface wind speeds at a height of 10 m (U ₁₀) based on TS-X data obtained with VV polarization in the ScanSAR, StripMap and Spotlight modes. The X-band GMF was validated by comparing the retrieved wind speeds from the TS-X data with in situ observations, the high-resolution limited area model (HIRLAM) and QuikSCAT scatterometer measurements. The bias and root mean square (RMS) values were 0.03 and 2.33 m s^?1, respectively, when compared with the co-located wind measurements derived from QuikSCAT. To apply the newly developed GMF to the TS-X data obtained in HH polarization, we analysed the C-band SAR polarization models and extended them to the X-band SAR data. The sea surface wind speeds were retrieved using the X-band GMF from pairs of TS-X images obtained in dual-polarization mode (i.e. VV and HH). The retrieved results were also validated by comparing with QuikSCAT measurements and the results of the German Weather Service (DWD) atmospheric model. The obtained RMS was 2.50 m s^?1 when compared with the co-located wind measurements derived from the QuikSCAT, and the absolute error was 2.24 m s^?1 when compared with DWD results.     

Quantifying scaling effects on satellite‐derived forest area estimates for the conterminous USA

We quantified the scaling effects on forest area estimates for the conterminous USA using regression analysis and the National Land Cover Dataset 30 m satellite‐derived maps in 2001 and 1992. The original data were aggregated to: (1) broad cover types (forest vs. non‐forest); and (2) coarser resolutions (1 km and 10 km). Standard errors of the model estimates were 2.3% and 4.9% at 1 km and 10 km resolutions, respectively. Our model improved the accuracies for 1 km by 0.6% (12 556 km²) in 2001 and 1.9% (43 198 km²) in 1992, compared to the forest estimates before the adjustments. Forest area observed from Moderate Resolution Imaging Spectroradiometer (MODIS) 2001 1 km land‐cover map for the conterminous USA might differ by 80 811 km² from what would be observed if MODIS was available at 30 m. Of this difference, 58% (46 870 km²) could be a relatively small net improvement, equivalent to 1444 Tg (or 1.5%) of total non‐soil forest CO₂ stocks. With increasing attention to accurate monitoring and evaluation of forest area changes for different regions of the globe, our results could facilitate the removal of bias from large‐scale estimates based on remote sensors with coarse resolutions.     

A geometrical optics model based on the non-Gaussian probability density distribution of sea surface slopes for wind speed retrieval at low incidence angles

In this study, a large amount of data from precipitation radar (PR) and National Data Buoy Center (NDBC) buoys are collocated for the development and validation of a Geometrical Optics Model, in order to retrieve wind speed at small incidence angles. The omni-directional model is developed based on the combination of the quasi-specular scattering theory and non-Gaussian probability density distribution of ocean surface slope, and can be applied at incidence angles as high as 15°. There are four parameters included in the proposed model: the effective Fresnel reflection coefficient, the mean square slope, and the two coefficients associated with the kurtosis of the sea surface slope distribution. Using one half of the collocated data, the dependence of the four parameters on the in situ wind speed is acquired. The results show that the effective Fresnel reflection coefficient has a decrease relative to that obtained in previous studies. We combine the proposed model with the maximum-likelihood estimation (MLE) technique to retrieve the ocean surface wind speed at the 10 m height. The retrieved wind speeds are then validated against those measured by the NDBC buoys. The comparison shows that the root mean square error (RMSE) and bias between the model retrievals and buoy observations are 1.54 m s^–1 and 0.1 m s^–1, respectively, revealing high agreements in the wind speed estimations. The results of this study indicate that the proposed model and the PR measurements at low incidence angles can provide reasonably accurate estimates of the surface wind speeds within the range of 0–20 m s^–1.     

Spatial variability of SEBAL estimated root-zone soil moisture across scales

This study investigated the spatial scaling behaviour of root-zone soil moisture obtained from optical/thermal remote-sensing observations. The data for this study were obtained from Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites on five different dates between early spring (April) and fall (September) in the years from 2000 to 2004 in the semi-arid middle Rio Grande Valley of New Mexico. Soil moisture data were obtained using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm. The data were spatially aggregated and checked for power-law behaviour over a range of scales from 30 m to 15 km for Landsat and from 1 to 28 km for MODIS images. Results of this study demonstrate that power-law scaling of soil moisture in the middle Rio Grande area holds up to 1 km² pixel size, but is no longer valid beyond that scale. Whereas previous studies have studied soil moisture in the top 5 cm of the soil using radar and point measurements, our study uses SEBAL to estimate root-zone soil moisture. Our study is consistent with these previous studies in showing that variation in root-zone soil follows an empirical power law for pixel sizes of up to about 10⁶ m² and that there is an apparent break in the scaling at larger scales.     

A northerly winter monsoon surge over the South China Sea studied by remote sensing and a numerical model

A northerly winter monsoon surge, which occurred on 15 December 2009 over the South China Sea (SCS), is studied by using satellite-based and ground-based remote-sensing data and an atmospheric numerical model. The remote-sensing data are from the advanced synthetic aperture radar (ASAR) onboard the Environmental Satellite (Envisat), the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite, the imager onboard the geostationary satellite MTSAT-1R (Multi-Functional Transport Satellite-1R) and the weather radar of the Hong Kong Observatory (HKO). A northerly winter monsoon surge is a cold air outbreak associated with a northerly wind, the passage of a cold front from north to south and a strong drop in air temperature. The analyses of the weather radar and the MTSAR-1R images of 15 December show that the surge of 15 December was associated with a rain band and a cloud front travelling over the SCS in a southeastward direction. Due to the interaction of the cold air (13°C) with the warm water (19°C), they dissolved when they had reached an offshore distance of approximately 160 km. The high-resolution (150 m) ASAR image reveals fine-scale features of the wind field, in particular details of the wind front, such as embedded rain cells and atmospheric gravity waves. Quantitative information on the near-surface wind field is retrieved from the ASAR, and it is shown that the wind field associated with the surge is quite variable and that speeds up to 15 m s^–1 are encountered in coastal wind jets. Finally, the remote-sensing data are compared with the simulation results of the pre-operational version of the Atmospheric Integrated Rapid-cycle (AIR) forecast model of the HKO. It is shown that, in general, the AIR model reproduces quite well the observational data.     

Altitudinal and temporal evolution of raindrop size distribution observed over a tropical station using a K-band radar

Rain drop size distribution (DSD) measurements at different heights were made using a micro rain radar (MRR) at Thiruvananthapuram (latitude: 8.3° N, longitude: 76.9° E). Rain DSD data obtained from the MRR have been compared with a Joss–Waldvogel impact-type disdrometer (RD-80) deployed nearby and found to have good agreement. The analysis uses data collected during 16 continuous rainfall episodes during the southwest monsoon (June to September, JJAS) season. Since all the episodes behaved similarly, a single continuous rainfall episode occurring from 1610:01 to 1612:31 hours Indian Standard Time (IST) on 12 August 2006 is presented here. The fall velocity of those drops that contributed most to the rain rate was more or less constant at different altitudes and also with time during this episode, and the average value was 4.65 m s^?1. The rain rate (RR) was below 5 mm h^?1 for all the heights throughout the time. At the beginning of the rain episode, the number of drops at any given altitude was lower for larger drops. But towards the end of the episode, the number of drops in the smallest size class had reduced at almost all heights, whereas the number of drops in the larger size classes had increased. This suggests that the larger drops coming from above on colliding with smaller drops could coalesce, thus sweeping out the smaller drops as they fall. The reduction of small drops is seen with a corresponding increase in larger drops and without increase also during the course of a rainfall event. The former is an indication of coalescence while the latter is that of evaporation. All these observed phenomena in the natural rain support the observations made by Low and List in 1982.