Climatology of mountain waves over Indian region based on the study of NOAA satellite imageries

NOAA (AVHRR) imageries have been studied for four years from 1982 to 1985. Overlapping wave zones in different imageries of the same fortnight have been given successively higher weightage in the scale of 1 to 7. Different monochromatic hatching scheme for different weight has been adopted. Direction of wind at the level of wave and its wavelength has also been marked over the region. Thus, the climatology has been documented into 24 fortnights. The following two periods emerge, while waves are generally seen over the region. 16 November to 15 April- This includes Winter Season upto the beginning of pre-monsoon. 01 June to 15 October -This includes Southwest monsoon upto the third week of postmonsoon. The occurrence of lee waves during the transition period is less. They are the second fortnight of April till the end of May and the second fortnight of October till the middle of November. A scrupulous observation shows that during both the periods (i) and (ii) mentioned above there is systematic eastward shift of the leewave zones with gradual induction of different airmasses over Indian subcontinent from beginning till end. A pictorial representation of the waves over India (West of 90° E Long.) is presented here.

Bay of Bengal cyclone cluster of November 1992

The cyclone cluster that formed over the Bay of Bengal during November 1992 has been looked into in relation to sea surface temperature (SST) distribution obtained from long. 1°x1° grid averages remotely sensed by NOAA-AVHRR. Examination of weekly SSTs has revealed that entire Bay of Bengal north of 8°N was unusually warmer about a week before the foundation of the cyclone cluster. As a matter of fact, during the above period the SST for each of the 60 grids over the sea area between 13°- 21° and 81°- 90°E exceeded 30°C. Widespread cooling of the sea was noticed just before the commencement of cyclogenesis.

Characterization of vegetation fraction estimated using spot-vegetation NDVI data for regional climate modeling in India

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Mapping of Sea Surface Temperature Distribution, Sibolga City, North Sumatera Province using NOAA/AVHRR Satellite

The research was aimed to map the sea surface temperature (SST) distribution in Sibolga waters that based on 20 years satellite image of NOAA/AVHRR. It used survey method for ground check in the field to collect in situ SST and other seawater parameters such as its visibillity, pH, and salinity. It found that the SST changes on each 5 year’s calculations with different pattern of distribution; the figures of SST ranged between 28.5-30 oC, 30.5-31 oC, 27-29 oC, and 27.5-28.5 oC. In addition, the pH of seawater ranged from 6-7 and 27-30 ppt in average. Different pattern of SST distribution might be related to global change on temperature and season over 20 years of study.

Global long-term land surface temperature for NOAA AVHRR: extension from 1981-2000 to 1981-2020

&lt;p&gt;As an important indicator of land-atmosphere energy interaction, land surface temperature (LST) plays an important role in the research of climate change, hydrology, and various land surface processes. Compared with traditional ground-based observation, satellite remote sensing provides the possibility to retrieve LST more efficiently over a global scale. Since the lack of global LST before, Ma et al., (2020) released a global 0.05 &amp;#215;0.05&amp;#160; long-term (1981-2000) LST based on NOAA-7/9/11/14 AVHRR. The dataset includes three layers: (1) instantaneous LST, a product generated based on an ensemble of several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST at 14:30 solar time; and (3) monthly averages of ODC LST. To meet the requirement of the long-term application, e.g. climate change, the period of the LST is extended from 1981-2000 to 1981-2020 in this study. The LST from 2001 to 2020 are retrieved from NOAA-16/18/19 AVHRR with the same algorithm for NOAA-7/8/11/14 AVHRR. The train and test results based on the simulation data from SeeBor and TIGR atmospheric profiles show that the accuracy of the RF-SWA method for the three sensors is consistent with the previous four sensors, i.e. the mean bias error and standard deviation less than 0.10 K and 1.10 K, respectively, under the assumption that the maximum emissivity and water vapor content uncertainties are 0.04 and 1.0 g/cm&lt;sup&gt;2&lt;/sup&gt;, respectively. The preliminary validation against &lt;em&gt;in-situ&lt;/em&gt; LST also shows a similar accuracy, indicating that the accuracy of LST from 1981 to 2020 are consistent with each other. In the generation code, the new LST has been improved in terms of land surface emissivity estimation, identification of cloud pixel, and the ODC method in order to generate a more reliable LST dataset. Up to now, the new version LST product (1981-2020) is under generating and will be released soon in support of the scientific research community.&lt;/p&gt;

NOAA-AVHRR Orbital Drift Correction: Validating Methods Using MSG-SEVIRI Data as a Benchmark Dataset

National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer (NOAA-AVHRR) data provides the possibility to build the longest Land Surface Temperature (LST) dataset to date, starting in 1981 up to the present. However, due to the orbital drift of the NOAA platforms, no LST dataset is available before 2000 and the arrival of newer platforms. Although numerous methods have been developed to correct this orbital drift effect on the LST, a lack of validation has prevented their application. This is the gap we bridge here by using the 15 min temporal resolution of Meteosat Second Generation–Spinning Enhanced Visible and Infra-Red Imager (MSG-SEVIRI) data to simulate drifted and reference LST time series. We then use these time series to validate an orbital drift correction method based on solar zenith angle (SZA) anomalies that we presented in a previous work (C1), as well as two variations of this approach (C0 and C2). Our results show that the C0 method performs better than the two others, although its overall bias absolute value ranges up to 1 K, while standard deviation values remain around 3 K. This is verified for most land covers, for all NOAA platforms, and these statistics remain mostly stable with noise on SZA time series (from 0° to ±10°). With this study, we show that orbital drift correction methods can be thoroughly validated and that such validation should aim toward bias absolute values below 0.1 K and standard deviation values around 1.4 K at coarse spatial resolution. To validate other orbital drift correction approaches, the drifted and reference time series used in this work are freely available for download from the first author’s webpage. This will be the first step toward the building of an orbital-drift-corrected long-term LST dataset.

A Dual-Channel Aerosol Optical Depth Retrieval Algorithm Incorporating the BRDF Effect from AVHRR over Eastern Asia

A NOAA/AVHRR dual-channel method over land is proposed to simultaneously retrieve aerosol optical depth (AOD) at 0.55 μm, and surface reflectance at 0.63 and 0.85 μm. Compared with previous well-established one-channel retrieval algorithms, this algorithm takes advantage of the surface reflectance ratio between 0.63 and 0.85 μm in an attempt to account for the effect induced by the surface bidirectional reflectance distribution function (BRDF). This effect cannot be negligible due to the orbit drift and phasing running of NOAA satellites, both of which potentially cause large solar angular variation. Meanwhile, the observation posture change of AVHRR would cause large sensor angular variation in time series measurements. The used surface reflectance ratio based on dual channels at 0.63 and 0.85 μm is found more reasonable to be assumed as unchanged during a certain period of time, compared to the traditional ratio when addressing the BRDF issue. AOD retrievals have been carried out over Eastern Asia. Validation against aerosol robotic network (AERONET) measurements shows that up to 83% of AOD validation collocations are within error lines (±0.15 ± 0.15τ, τ is AOD) with an R of 0.88 and an root mean square error (RMSE) of 0.15. The dual-channel algorithm taking into account the surface BRDF effect is proved outperforming the conventional 0.63 μm-channel method. It indicates that our algorithm has the potential to be applied to the retrieval of long series of AOD, especially to the AOD retrieval of the sensors which lack a shortwave infrared channel required in the MODerate resolution Imaging Spectroradiometer (MODIS) dark target AOD algorithm.