scholarly journals KARAKTERISTIK KETINGGIAN DASAR AWAN YANG DIUKUR DENGAN SENSOR INFRA MERAH RADIOMETER PADA PUNCAK MUSIM HUJAN DI JABODETABEK.

2020 ◽  
Vol 20 (1) ◽  
pp. 39-45
Author(s):  
Findy Renggono
Keyword(s):  
Lapse Rate ◽  
Cloud Base ◽  
Base Temperature ◽  
Cloud Detection ◽  
Weather Modification ◽  
Temperature Lapse Rate ◽  
Cloud Properties ◽  
Comparison Results ◽  
Rain Radar ◽  
Cloud Base Height

IntisariInformasi mengenai tinggi dasar awan penting bagi penelitian atmosfer dan juga sebagai masukan bagi pemodelan cuaca. Pada kegiatan modifikasi cuaca, informasi ini juga sangat penting dalam menentukan awan yang akan disemai. Dalam tulisan ini, pengukuran tinggi dasar awan dilakukan dengan menggunakan sensor infra merah yang terpasang pada radiometer. Sensor infra merah ini akan mengukur suhu dasar awan yang kemudian dapat diketahui ketinggiannya dengan melihat temperatur lapse rate. Hasil pengukuran dibandingkan dengan hasil pengamatan awan oleh micro rain radar yang terletak di lokasi yang sama. Hasil pengukuran dari kedua peralatan ini menunjukkan kesesuaian antara kemunculan awan pada micro rain radar yang ditunjukkan dengan struktur vertikal awan dengan hasil pengamatan dengan IRT dari radiometer. Pengamatan selama puncak musim hujan di Jabodetabek (Januari – Maret 2019) menunjukan adanya pola harian yang cukup jelas. AbstractInformation on cloud properties is important for atmospheric research and as well as for weather modeling. In weather modification, this information is very important for cloud seeding strategy. The observation of cloud base height is carried out using infrared sensors mounted on a radiometer. These infrared thermometer sensors are capable of detecting the cloud base temperature, the cloud base height is obtained by looking at the temperature lapse rate retrieved from radiometer observation. The results were compared with the cloud observation by micro rain radar which is located at the same location. The comparison results of these two instruments show that the consistency of cloud detection was good. Based on the observation during the peak of the rainy season in Jabodetabek (January-March 2019), it is shown a fairly clear daily pattern

A Validation of CloudSat and CALIPSO's Temperature, Humidity, Cloud Detection, and Cloud Base Height over the Arctic Marine Cryosphere

2013 ◽  
Vol 51 (3) ◽  
pp. 249-264 ◽  
Author(s):  
Lauren M. Candlish ◽  
Richard L. Raddatz ◽  
Geoffrey G. Gunn ◽  
Matthew G. Asplin ◽  
David G. Barber
Keyword(s):  
The Arctic ◽  
Cloud Base ◽  
Cloud Detection ◽  
Cloud Base Height

Using Radio Occultation to Detect Clouds in the Middle and Upper Troposphere

2021 ◽  
Author(s):  
Jeana Mascio ◽  
Stephen S. Leroy ◽  
Robert P. d’Entremont ◽  
Thomas Connor ◽  
E. Robert Kursinski
Keyword(s):  
Relative Humidity ◽  
High Performance ◽  
Radio Occultation ◽  
Three Dimensional ◽  
Roc Curves ◽  
Lapse Rate ◽  
Cloud Detection ◽  
Upper Troposphere ◽  
Temperature Lapse Rate ◽  
Direct Sensitivity

AbstractRadio occultation (RO) measurements have little direct sensitivity to clouds, but recent studies have shown that they may have an indirect sensitivity to thin, high clouds that are difficult to detect using conventional passive space-based cloud sensors. We implement two RO-based cloud detection (ROCD) algorithms for atmospheric layers in the middle and upper troposphere. The first algorithm is based on the methodology of a previous study, which explored signatures caused by upper tropospheric clouds in RO profiles according to retrieved relative humidity, temperature lapse rate, and gradients in log-refractivity (ROCD-P), and the second is based on inferred relative humidity alone (ROCD-M). In both, atmospheric layers are independently predicted as cloudy or clear based on observational data, including high performance RO retrievals. In a demonstration, we use data from 10 days spanning seven months in 2020 of FORMOSAT-7/COSMIC-2. We use the forecasts of NOAA GFS to aid in the retrieval of relative humidity. The prediction is validated with a cloud truth dataset created from the imagery of the GOES-16 Advanced Baseline Imager (ABI) satellite and the GFS three-dimensional analysis of cloud state conditions. Given these two algorithms for the presence or absence of clouds, confusion matrices and receiver operating characteristic (ROC) curves are used to analyze how well these algorithms perform. The ROCD-M algorithm has a balanced accuracy, which defines the quality of the classification test that considers both the sensitivity and specificity, greater than 70% for all altitudes between 6 and 10.25 km.

scholarly journals Temporal Variability of Fair-Weather Cumulus Statistics at the ACRF SGP Site

2008 ◽  
Vol 21 (13) ◽  
pp. 3344-3358 ◽  
Author(s):  
Larry K. Berg ◽  
Evgueni I. Kassianov
Keyword(s):  
Great Plains ◽  
Sensible Heat Flux ◽  
Cloud Fraction ◽  
Surface Fluxes ◽  
Chord Length ◽  
Cloud Base ◽  
Fair Weather ◽  
Cloud Properties ◽  
Cloud Thickness ◽  
Cloud Base Height

Abstract Continental fair-weather cumuli exhibit significant diurnal, day-to-day, and year-to-year variability. This study describes the climatology of cloud macroscale properties, over the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) Southern Great Plains (SGP) site. The diurnal cycle of cloud fraction, cloud-base height, cloud-top height, and cloud thickness were well defined. The cloud fraction reached its maximum value near 1400 central standard time. The average cloud-base height increased throughout the day, while the average cloud thickness decreased with time. In contrast to the other cloud properties, the average cloud-chord length remained nearly constant throughout the day. The sensitivity of the cloud properties to the year-to-year variability of precipitation and day-to-day changes in the height of the lifting condensation level (zLCL) and surface fluxes were compared. The cloud-base height was found to be sensitive to both the year, zLCL, and the surface fluxes of heat and moisture; the cloud thickness was found to be more sensitive to the year than to zLCL; the cloud fraction was sensitive to both the low-level moisture and the surface sensible heat flux; and cloud-chord length was sensitive to zLCL. Distributions of the cloud-chord length over the ACRF SGP site were computed and were well fit by an exponential distribution. The contribution to the total cloud fraction by clouds of each cloud-chord length was computed, and it was found that the clouds with a chord length of about 1 km contributed most to the observed cloud fraction. This result is similar to observations made with other remote sensing instruments or in modeling studies, but it is different from aircraft observations of the contribution to the total cloud fraction by clouds of different sizes.

scholarly journals How Well Do Regional Climate Models Reproduce Radiation and Clouds in the Arctic? An Evaluation of ARCMIP Simulations

2008 ◽  
Vol 47 (9) ◽  
pp. 2405-2422 ◽  
Author(s):  
Michael Tjernström ◽  
Joseph Sedlar ◽  
Matthew D. Shupe
Keyword(s):  
Heat Budget ◽  
Regional Climate ◽  
Cloud Water ◽  
Surface Radiation ◽  
The Arctic ◽  
Cloud Base ◽  
Base Temperature ◽  
Model Errors ◽  
Cloud Properties ◽  
Low Clouds

Abstract Downwelling radiation in six regional models from the Arctic Regional Climate Model Intercomparison (ARCMIP) project is systematically biased negative in comparison with observations from the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment, although the correlations with observations are relatively good. In this paper, links between model errors and the representation of clouds in these models are investigated. Although some modeled cloud properties, such as the cloud water paths, are reasonable in a climatological sense, the temporal correlation of model cloud properties with observations is poor. The vertical distribution of cloud water is distinctly different among the different models; some common features also appear. Most models underestimate the presence of high clouds, and, although the observed preference for low clouds in the Arctic is present in most of the models, the modeled low clouds are too thin and are displaced downward. Practically all models show a preference to locate the lowest cloud base at the lowest model grid point. In some models this happens also to be where the observations show the highest occurrence of the lowest cloud base; it is not possible to determine if this result is just a coincidence. Different factors contribute to model surface radiation errors. For longwave radiation in summer, a negative bias is present both for cloudy and clear conditions, and intermodel differences are smaller when clouds are present. There is a clear relationship between errors in cloud-base temperature and radiation errors. In winter, in contrast, clear-sky cases are modeled reasonably well, but cloudy cases show a very large intermodel scatter with a significant bias in all models. This bias likely results from a complete failure in all of the models to retain liquid water in cold winter clouds. All models overestimate the cloud attenuation of summer solar radiation for thin and intermediate clouds, and some models maintain this behavior also for thick clouds.

scholarly journals Cloud base height retrieval from multi-angle satellite data

2019 ◽  
Vol 12 (3) ◽  
pp. 1841-1860 ◽  
Author(s):  
Christoph Böhm ◽  
Odran Sourdeval ◽  
Johannes Mülmenstädt ◽  
Johannes Quaas ◽  
Susanne Crewell
Keyword(s):  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Cloud Base ◽  
Height Distribution ◽  
Cloud Properties ◽  
Scene Structure ◽  
Global Coverage ◽  
Terra Satellite ◽  
High Clouds ◽  
Cloud Base Height

Abstract. Clouds are a key modulator of the Earth energy budget at the top of the atmosphere and at the surface. While the cloud top height is operationally retrieved with global coverage, only few methods have been proposed to determine cloud base height (zbase) from satellite measurements. This study presents a new approach to retrieve cloud base heights using the Multi-angle Imaging SpectroRadiometer (MISR) on the Terra satellite. It can be applied if some cloud gaps occur within the chosen distance of typically 10 km. The MISR cloud base height (MIBase) algorithm then determines zbase from the ensemble of all MISR cloud top heights retrieved at a 1.1 km horizontal resolution in this area. MIBase is first calibrated using 1 year of ceilometer data from more than 1500 sites within the continental United States of America. The 15th percentile of the cloud top height distribution within a circular area of 10 km radius provides the best agreement with the ground-based data. The thorough evaluation of the MIBase product zbase with further ceilometer data yields a correlation coefficient of about 0.66, demonstrating the feasibility of this approach to retrieve zbase. The impacts of the cloud scene structure and macrophysical cloud properties are discussed. For a 3-year period, the median zbase is generated globally on a 0.25∘ × 0.25∘ grid. Even though overcast cloud scenes and high clouds are excluded from the statistics, the median zbase retrievals yield plausible results, in particular over ocean as well as for seasonal differences. The potential of the full 16 years of MISR data is demonstrated for the southeast Pacific, revealing interannual variability in zbase in accordance with reanalysis data. The global cloud base data for the 3-year period (2007–2009) are available at https://doi.org/10.5880/CRC1211DB.19.

An Assessment of Numerical Weather Prediction–Derived Low-Cloud-Base Height Forecasts

2015 ◽  
Vol 30 (2) ◽  
pp. 486-497 ◽  
Author(s):  
Mana Inoue ◽  
Alexander D. Fraser ◽  
Neil Adams ◽  
Scott Carpentier ◽  
Helen E. Phillips
Keyword(s):  
Optical Depth ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Radiosonde Data ◽  
Cloud Base ◽  
Numerical Weather ◽  
Cloud Properties ◽  
Low Cloud ◽  
Height Prediction ◽  
Cloud Base Height

Abstract As demand for flight operations in Antarctica grows, accurate weather forecasting of cloud properties such as extent, cloud base, and cloud-top altitude becomes essential. The primary aims of this work are to ascertain relationships between numerical weather prediction (NWP) model output variables and surface-observed cloud properties and to develop low-cloud-base (<2000 m) height prediction algorithms for use across Antarctica to assist in low-cloud forecasting for aircraft operations. NWP output and radiosonde data are assessed against surface observations, and the relationship between the relative humidity RH profile and the height of the observed low-cloud base is investigated. The ability of NWP-derived RH and ice–water cloud optical depth profiles to represent the observed low-cloud conditions around each of the three Australian stations in East Antarctica is assessed. NWP-derived RH is drier than that reported by radiosonde from ground level up to ~2000 m. This trend reverses in the higher troposphere, and the largest positive difference is observed at ~10 000 m. A consequence is very low RH thresholds are needed for low-cloud-base height prediction using NWP RH profiles. RH and optical depth–based threshold techniques all show skill in reproducing the observed cloud-base height at all Australian Antarctic stations, but the radiosonde-derived RH technique is superior in all cases. This comparison of three low-cloud-base height retrieval techniques provides the first documented assessment of the relative efficacy of each technique in Antarctica.

scholarly journals Cloud climatologies from the InfraRed Sounders AIRS and IASI: Strengths, Weaknesses and Applications

2017 ◽  
Author(s):  
Claudia J. Stubenrauch ◽  
Artem G. Feofilov ◽  
Sofia E. Protopapadaki ◽  
Raymond Armante
Keyword(s):  
Cloud Amount ◽  
Co2 Concentration ◽  
Boreal Summer ◽  
Cloud Base ◽  
Cloud Detection ◽  
Ancillary Data ◽  
Total Cloud Amount ◽  
Low Level ◽  
Cloud Properties ◽  
High Level

Abstract. The cloud retrieval scheme developed at the Laboratoire de Météorologie Dynamique (LMD) can now be easily adapted to any Infrared (IR) sounder: the CIRS (Clouds from IR Sounders) retrieval applies improved radiative transfer, as well as an original method accounting for atmospheric spectral transmissivity changes associated with CO2 concentration. The latter is essential when considering long-term time series of cloud properties. For the 13-year and 8-year global climatologies of cloud properties from observations of the Atmospheric IR Sounder (AIRS) and of the IR Atmospheric Interferometer (IASI), respectively, we used the latest ancillary data (atmospheric profiles, surface emissivities and atmospheric spectral transmissivities). The A-Train active instruments, lidar and radar of the CALIPSO and CloudSat missions, provide a unique opportunity to evaluate the retrieved AIRS cloud properties such as cloud amount and height as well as to explore the vertical structure of different cloud types. CIRS cloud detection agreement with CALIPSO-CloudSat is about 84%–85% over ocean, 79%–82% over land and 70%–73% over ice / snow, depending on atmospheric ancillary data. Global cloud amount has been estimated to 67%–70%. CIRS cloud height coincides with the middle between the cloud top and the apparent cloud base (real base for optically thin clouds or height at which the cloud reaches opacity) independent of cloud emissivity, which is about 1 km below cloud top for low-level clouds and about 1.5 km to 2.5 km below cloud top for high-level clouds, slightly increasing because the apparent vertical cloud extent is slightly larger for large cloud emissivity. IR sounders are in particular advantageous for the retrieval of upper tropospheric cloud properties, with a reliable cirrus identification down to an IR optical depth of 0.1, day and night. Total cloud amount consists of about 40% high-level clouds and about 40% low-level clouds and 20% mid-level clouds, the latter two only detected when not hidden by upper clouds. Upper tropospheric clouds are most abundant in the tropics, where high opaque clouds make out 7.5%, thick cirrus 27.5% and thin cirrus about 21.5% of all clouds. The asymmetry in upper tropospheric cloud amount between Northern and Southern hemisphere with annual mean of 5% has a pronounced seasonal cycle with a maximum of 25% in boreal summer, which can be linked to the shift of the ITCZ peak latitude. Comparing tropical geographical change patterns of high opaque clouds with that of thin cirrus as a function of changing tropical mean surface temperature indicates that their response to climate change may be quite different, with potential consequences on the atmospheric circulation.

scholarly journals Cloud-Base Height Derived from a Ground-Based Infrared Sensor and a Comparison with a Collocated Cloud Radar

2018 ◽  
Vol 35 (4) ◽  
pp. 689-704 ◽  
Author(s):  
Zhe Wang ◽  
Zhenhui Wang ◽  
Xiaozhong Cao ◽  
Jiajia Mao ◽  
Fa Tao ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Microwave Radiometer ◽  
Radar Data ◽  
Cloud Base ◽  
Cloud Detection ◽  
Cloud Radar ◽  
Thin Cloud ◽  
Reflectivity Data ◽  
Downward Radiation ◽  
Cloud Base Height

AbstractAn improved algorithm to calculate cloud-base height (CBH) from infrared temperature sensor (IRT) observations that accompany a microwave radiometer was described, the results of which were compared with the CBHs derived from ground-based millimeter-wavelength cloud radar reflectivity data. The results were superior to the original CBH product of IRT and closer to the cloud radar data, which could be used as a reference for comparative analysis and synergistic cloud measurements. Based on the data obtained by these two kinds of instruments for the same period (January–December 2016) from the Beijing Nanjiao Weather Observatory, the results showed that the consistency of cloud detection was good and that the consistency rate between the two datasets was 81.6%. The correlation coefficient between the two CBH datasets reached 0.62, based on 73 545 samples, and the average difference was 0.1 km. Higher correlations were obtained for thicker clouds with a larger echo intensity. A low-level thin cloud cannot be regarded as a blackbody because of its high transmittance, which results in higher CBHs derived from IRT data. Because of a smaller cloud radiation effect for high-level thin cloud above 8 km, the contribution of the atmospheric downward radiation below the cloud base to the IRT cannot be ignored, as it results in lower CBHs derived from IRT data. Owing to the seasonal variation of atmospheric downward radiation reaching the IRT, the difference between the two CBHs also has a seasonal variation. The IRT CBHs are generally higher (lower) than the cloud radar CBHs in winter (summer).

Infrared Thermal Imagery of Cloud Base in Tornadic Supercells

2006 ◽  
Vol 23 (11) ◽  
pp. 1445-1461 ◽  
Author(s):  
Robin L. Tanamachi ◽  
Howard B. Bluestein ◽  
Stephen S. Moore ◽  
Robert P. Madding
Keyword(s):  
Infrared Camera ◽  
Temperature Gradients ◽  
Lapse Rate ◽  
Dust Particles ◽  
Cloud Base ◽  
Base Temperature ◽  
Infrared Images ◽  
Supercell Thunderstorms ◽  
Infrared Emissions ◽  
Horizontal Temperature Gradients

Abstract During the spring seasons of 2003 and 2004, an infrared thermal camera was deployed in and around supercell thunderstorms in an attempt to retrieve the temperature at the cloud base of a mesocyclone prior to tornadogenesis. The motivation for this exercise was to obtain temperature information that might indicate the thermal structure, timing, and extent of the rear-flank downdraft (RFD) and possibly elucidate its relationship to tornadogenesis. An atmospheric transmissivity study was conducted to account for the effects of atmospheric transmission on the measured temperatures, and to determine an ideal range of distances from which infrared images of a wall cloud or a tornado could be safely captured while still retrieving accurate cloud temperatures. This range was found to be 1.5–3 km. Two case days are highlighted in which the infrared camera was deployed within 1.5–3 km of a tornado; the visible and infrared images are shown side by side for comparison. On the single occasion on which the tornadogenesis phase was captured, the infrared images show no strong horizontal temperature gradients. From the infrared images taken of tornadoes, it can be inferred that the infrared signal from the tornado consisted primarily of infrared emissions from lofted dust particles or cloud droplets, and that the infrared signal from the tornado condensation funnel was easily obscured by infrared emissions from lofted dust particles or intervening precipitation curtains. The deployment of the infrared camera near supercell thunderstorms and the analysis of the resulting images proved challenging. It is concluded that the infrared camera is a useful tool for measuring cloud-base temperature gradients provided that distance and viewing angle constraints are met and that the cloud base is unobscured by rain or other intervening infrared emission sources. When these restrictions were met, the infrared camera successfully retrieved horizontal temperature gradients along the cloud base and vertical temperature gradients (close to the moist adiabatic lapse rate) along the tornado funnel.

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