Estimation of divergent wind from OLR data for use in objective analysis over the Indian region

A scheme is formulated for the use of OLR data in the estimation of vertical velocity; divergence and then the divergent part of the wind over Indian region. In this scheme, ascending motion over cloudy region is estimated from an empirical relation between the cloud top temperature and descending motion over cloud-free region is estimated from the thermodynamic energy equation and both are blended. From this blended vertical velocity field, aivergence, velocity potential and divergent winds at all standard levels from 4 to 8 July 1979 at 00 UTC are computed. These fields are compared with satellite cloud pictures, rainfall etc and they are found to be realistic in depicting the synoptic conditions. Total wind is computed as the sum of the estimated divergent component and rotational component computed from observed wind field. For assessment of the scheme, this total wind field at 850 hPa is used as initial. guess field in univariate optimum interpolation scheme and analyses were made for the period 4 to 8 July 1979. Results show that scheme is able to produce realistic analyses which included divergent part of the wind.

Identifikasi Fenomena Mesoscale Convective System (MCC) di Selat Karimata

Abstract—Indonesia located in the equatorial region which has potential to have a major impact on atmospheric physical conditions during extreme weather events such as the Mesoscale Convective Complex (MCC). MCC is a phenomenon that was first discovered by (Maddox, 1980) where this phenomenon is characterized by the presence of a quasi-circular (almost circular) cloud shield with an eccentricity of 0.7 with a cloud cover area of 100,000 km², the cloud core area covers 50,000 km² and cloud top temperature IR1 -52 ℃. These cloud conditions last for a minimum of 6 hours and cause severe weather and extreme rain. This study aims to identify the MCC phenomenon in the Karimata Strait on 19-20 September 2020 which caused heavy rains in parts of the West coast of Kalimantan and Bangka Island using Himawari-8 Satellite imagery data and the MATLAB application. The results showed that on September 19, MCC was identified at 09.00-19.00 UTC, then on September 20, MCC was identified at 16.00-23.00 UTC. At the time of the MCC event, Bangka and Pontianak regions experienced extreme rains recorded on AWS Digi Stamet Pontianak with rainfall reaching 43.4 mm/hour and ARG Lubuk Besar Bangka Tengah with rainfall reaching 16.8 mm/hour. Keywords: mesoscale convective complex (MCC), himawari-8, MATLAB Abstrak—Indonesia merupakan negara yang terletak diwilayah ekuator dimana berpotensi memiliki dampak besar terhadap kondisi fisik atmosfer saat terjadi cuaca ekstrem seperti Mesoscale Convective Complex (MCC). MCC merupakan fenomena yang pertama kali ditemukan oleh (Maddox, 1980) dimana fenomena ini dicirikan dengan adanya perisai awan yang berbentuk quasi circular (hampir lingkaran) dengan eksentrisitas ≥ 0,7 dengan luas area selimut awan ≥ 100.000 km² , luas area inti awan mencakup ≥ 50.000 km² serta suhu puncak awan IR1 ≤ -52 ℃. Kondisi awan tersebut bertahan minimun selama 6 jam dan menyebabkan cuaca buruk dan hujan ekstrem. Penelitian ini bertujuan untuk mengidentifikasi fenomena MCC di Selat Karimata pada Tanggal 19-20 September 2020 yang menyebabkan hujan lebat di sebagian wilayah Kalimantan bagian pesisir Barat dan Pulau Bangka menggunakan data citra Satelit Himawari-8 dan aplikasi MATLAB. Hasil penelitian menunjukkan pada tanggal 19 September, MCC teridentifikasi pada pukul 09.00-19.00 UTC selanjutnya tanggal 20 September 2020 MCC teridentifikasi pada pukul 16.00-23.00 UTC. Pada saat peristiwa MCC, wilayah Bangka dan Pontianak mengalami hujan ekstrem yang tercatat pada AWS Digi Stasiun Meteorologi Pontianak dengan curah hujan mencapai 43,4 mm/jam dan ARG Lubuk Besar Bangka Tengah dengan curah hujan mencapai 16,8 mm/jam. Kata kunci: mesoscale convective complex (MCC), himawari-8, MATLAB

Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using Machine Learning Algorithm

Satellite rainrate estimation is a great challenge, especially in mesoscale convective systems (MCSs), which is mainly due to the absence of a direct physical connection between observable cloud parameters and surface rainrate. The machine learning technique was employed in this study to estimate rainrate in the MCS domain via using cloud top temperature (CTT) derived from a geostationary satellite. Five kinds of machine learning models were investigated, i.e., polynomial regression, support vector machine, decision tree, random forest, and multilayer perceptron, and the precipitation of Climate Prediction Center morphing technique (CMORPH) was used as the reference. A total of 31 CTT related features were designed to be the potential inputs for training an algorithm, and they were all proved to have a positive contribution in modulating the algorithm. Random forest (RF) shows the best performance among the five kinds of models. By combining the classification and regression schemes of the RF model, an RF-based hybrid algorithm was proposed first to discriminate the rainy pixel and then estimate its rainrate. For the MCS samples considered in this study, such an algorithm generates the best estimation, and its accuracy is definitely higher than the operational precipitation product of FY-4A. These results demonstrate the promising feasibility of applying a machine learning technique to solve the satellite precipitation retrieval problem.

RAINFALL THRESHOLDS FOR LANDSLIDE IN GARUT REGENCY, WEST JAVA USING HIMAWARI-8 DATA

Landslide was one of natural disasters that affected by the weather. The intensity of landslide in Indonesia tended to increase from year to year with a larger area distribution. Remote sensing was a method that can be used to support disaster mitigation and response activities including landslide because this technology allows monitoring and analysis both spatially and temporally. One of the remote sensing satellites that can be used for monitoring landslide was Himawari-8. This weather satellite was launched in 2014 and had a temporal resolution of 10 minutes making it effective for meteorological, environmental and disaster observations. This research has used Himawari-8 rainfall data which extracted from cloud top temperature to determine the intensity of rainfall that causes landslide in Garut Regency. The daily accumulation of rainfall for five days before the landslide event up to five days after the landslide event has been investigated statistically to analyze the conditions of rainfall that trigger landslides. Rainfall thresholds for landslide was determined by the intensity maximum of daily accumulation. It was found that the intensity of rainfall that has potential to cause landslides based on the threshold value is as follows: Malangbong District 60.3 mm/day, Banjarwangi District 32.3 mm/day, Pasirwangi District 36.9 mm/day, Cisewu District 35.1 mm/day and Talegong District 52.8 mm/day. Landslide in four districts have corresponded with the day where the intensity of rainfall was maximum. Meanwhile for Talegong District, the landslide was occurred a day after its maximum.Keywords: rainfall, Himawari-8, landslide, remote sensing, thresholdLongsor merupakan salah satu bencana alam yang dipengaruhi oleh cuaca. Intensitas longsor di Indonesia cenderung meningkat dari tahun ke tahun dengan sebaran wilayah yang lebih luas. Penginderaan jauh merupakan metode yang dapat digunakan untuk mendukung kegiatan mitigasi dan tanggap bencana termasuk longsor karena teknologi ini memungkinkan pemantauan dan analisis baik secara spasial maupun temporal. Salah satu satelit penginderaan jauh yang dapat digunakan untuk pemantauan longsor adalah Himawari-8. Satelit cuaca ini diluncurkan pada tahun 2014 dan memiliki resolusi temporal 10 menit sehingga efektif untuk pengamatan meteorologi, lingkungan dan bencana. Penelitian ini menggunakan data curah hujan Himawari-8 yang diekstrak dari suhu puncak awan untuk mengetahui intensitas curah hujan penyebab longsor di Kabupaten Garut. Akumulasi curah hujan harian selama lima hari sebelum kejadian longsor sampai dengan lima hari setelah kejadian longsor diteliti secara statistik untuk menganalisis kondisi curah hujan yang memicu terjadinya longsor. Ambang batas curah hujan untuk longsor ditentukan oleh intensitas maksimum akumulasi harian. Diketahui bahwa intensitas curah hujan yang berpotensi menimbulkan longsor berdasarkan nilai ambang batas adalah sebagai berikut: Kecamatan Malangbong 60,3 mm / hari, Kecamatan Banjarwangi 32,3 mm / hari, Kecamatan Pasirwangi 36,9 mm / hari, Kecamatan Cisewu 35,1 mm / hari dan Kecamatan Talegong 52,8 mm / hari. Tanah longsor di empat kecamatan telah sesuai dengan hari dimana intensitas curah hujan maksimal. Sedangkan untuk Kecamatan Talegong, longsor terjadi sehari setelah maksimumnya.Kata kunci: curah hujan, Himawari-8, longsor, penginderaan jauh, ambang batas

Signatures of Oceanic Wind Events in Geostationary Cloud Top Temperature and Lightning Data

Thirteen ocean-based wind events from 2018, detected by buoys and Coastal-Marine Automated Network (C-MAN) stations, were analyzed using 1 min mesoscale sector Advanced Baseline Imager (ABI) cloud top brightness temperature (CTTB) data, as well as 1 min Geostationary Lightning Mapper (GLM) lightning data. The ABI and GLM instruments are located on the Geostationary Operational Environmental Satellite (GOES)-16 satellite. An oceanic wind event was defined as a buoy or C-MAN station-recorded peak wind gust of at least 14 m s−1, associated with a convective storm. The wind gust was required to exceed the wind speed by at least 4 m s−1 at the time of the event, but not exceed the corresponding wind speed by at least 4 m s−1 for more than 30 min. This study hypothesized that prior to a wind event, there should be unique signatures in ABI CTTB and GLM lightning datasets. The presumption was that the minimum CTTB and maximum flash rate should occur near the same time and prior to the event. The minimum CTTB occurred an average of 10.5 min and a median of 7 min prior to events, with a range from 29 min prior to 1 min after the event. Changes in CTTB were often subtle. A maximum flash rate occurred within 5 min of the minimum CTTB for 11 of the 12 events with lightning, and did not exceed 11 fl min−1 for nine of the 12 events with lightning. Operational weather forecasters might use CTTB and lightning trends to help identify storms capable of producing significant oceanic wind events.

Properties of Arctic liquid and mixed-phase clouds from shipborne Cloudnet observations during ACSE 2014

This study presents Cloudnet retrievals of Arctic clouds from measurements conducted during a 3-month research expedition along the Siberian shelf during summer and autumn 2014. During autumn, we find a strong reduction in the occurrence of liquid clouds and an increase for both mixed-phase and ice clouds at low levels compared to summer. About 80 % of all liquid clouds observed during the research cruise show a liquid water path below the infrared black body limit of approximately 50 g m−2. The majority of mixed-phase and ice clouds had an ice water path below 20 g m−2. Cloud properties are analysed with respect to cloud-top temperature and boundary layer structure. Changes in these parameters have little effect on the geometric thickness of liquid clouds while mixed-phase clouds during warm-air advection events are generally thinner than when such events were absent. Cloud-top temperatures are very similar for all mixed-phase clouds. However, more cases of lower cloud-top temperature were observed in the absence of warm-air advection. Profiles of liquid and ice water content are normalized with respect to cloud base and height. For liquid water clouds, the liquid water content profile reveals a strong increase with height with a maximum within the upper quarter of the clouds followed by a sharp decrease towards cloud top. Liquid water content is lowest for clouds observed below an inversion during warm-air advection events. Most mixed-phase clouds show a liquid water content profile with a very similar shape to that of liquid clouds but with lower maximum values during events with warm air above the planetary boundary layer. The normalized ice water content profiles in mixed-phase clouds look different from those of liquid water content. They show a wider range in maximum values with the lowest ice water content for clouds below an inversion and the highest values for clouds above or extending through an inversion. The ice water content profile generally peaks at a height below the peak in the liquid water content profile – usually in the centre of the cloud, sometimes closer to cloud base, likely due to particle sublimation as the crystals fall through the cloud.

Towards parametrising atmospheric concentrations of ice nucleating particles active at moderate supercooling

A small fraction of freezing cloud droplets probably initiates much of the precipitation above continents. Only a minute fraction of aerosol particles, so-called ice nucleating particles (INPs), can trigger initial ice formation at −15 °C, a cloud-top temperature frequently associated with snowfall. We found at a mountain top site in the Swiss Alps that concentrations of INPs active at −15 °C are different functions of coarse (> 2 μm) aerosol particle concentrations, depending on whether an air mass is precipitating, non-precipitating, or carrying Saharan dust and non-precipitating. Consequently, we suggest that a parameterisation at moderate supercooling should consider coarse particles in combination with air mass differentiation.

A kernel-driven BRDF model to inform satellite-derived visible anvil cloud detection

Satellites routinely observe deep convective clouds across the world. The cirrus outflow from deep convection, commonly referred to as anvil cloud, has a ubiquitous appearance in visible and infrared (IR) wavelength imagery. Anvil clouds appear as broad areas of highly reflective and cold pixels relative to the darker and warmer clear sky background, often with embedded textured and colder pixels that indicate updrafts and gravity waves. These characteristics would suggest that creating automated anvil cloud detection products useful for weather forecasting and research should be straightforward, yet in practice such product development can be challenging. Some anvil detection methods have used reflectance or temperature thresholding, but anvil reflectance varies significantly throughout a day as a function of combined solar illumination and satellite viewing geometry, and anvil cloud top temperature varies as a function of convective equilibrium level and tropopause height. This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles, thereby addressing limitations of previous methods. A one-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bi-directional reflectance distribution function (BRDF) model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angle configurations, in addition to the reflectance uncertainty for each angular bin. Application of the BRDF model for cloud optical depth retrieval in deep convection is described as well.