Exploring the Potential of Statistical Modeling to Retrieve the Cloud Base Height from Geostationary Satellites: Applications to the ABI Sensor on Board of the GOES-R Satellite Series

Although cloud base height is a relevant variable for many applications, including aviation, it is not routinely monitored by current geostationary satellites. This is probably a consequence of the difficulty of providing reliable estimations of the cloud base height from visible and infrared radiances from current imagers. We hypothesize that existing algorithms suffer from the accumulation of errors from upstream retrievals necessary to estimate the cloud base height, and that this hampers higher predictability in the retrievals to be achieved. To test this hypothesis, we trained a statistical model based on the random forest algorithm to retrieve the cloud base height, using as predictors the radiances from Geostationary Operational Environmental Satellites (GOES-16) and variables from a numerical weather prediction model. The predictand data consisted of cloud base height observations recorded at meteorological aerodrome report (METAR) stations over an extended region covering the contiguous USA. Our results indicate the potential of the proposed methodology. In particular, the performance of the cloud base height retrievals appears to be superior to the state-of-the-science algorithms, which suffer from the accumulation of errors from upstream retrievals. We also find a direct relationship between the errors and the mean cloud base height predicted over the region, which allowed us to obtain estimations of both the cloud base height and its error.

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Prediction of Regional Global Horizontal Irradiance Combining Ground-Based Cloud Observation and Numerical Weather Prediction

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1073-1076.388 ◽

2014 ◽

Vol 1073-1076 ◽

pp. 388-394 ◽

Cited By ~ 4

Author(s):

Fang Cui ◽

Rong Rong Ju ◽

Yu Yu Ding ◽

Huang Ding ◽

Xu Cheng

Keyword(s):

Numerical Weather Prediction ◽

Weather Prediction ◽

Test Site ◽

Numerical Weather Prediction Model ◽

Cloud Base ◽

Short Term ◽

Numerical Weather ◽

Set Up ◽

Prediction Approach ◽

Cloud Base Height

This paper presents a novel very-short term GHI prediction approach which combines ground-based sky images derived cloud map forecast and cloud base height estimated using numerical weather prediction output. To achieve accurate cloud map forecast, a transformation of original sky images is proceeded to eliminate spherical and coordinate distortion. A WRF based numerical weather prediction model is set up to forecast the local meteorological parameters and estimate cloud base heights information. The cloud base height estimation is adopted to derive real locations and sizes of clouds, and eventually obtain very-short term forecast of the local global horizontal irradiance. The validation of the proposed method is carried out by comparing predicted and measured irradiance of a test site. The results show that the method has high prediction accuracy, and has ability to predict the radiation fluctuation caused by the cloud sheltering process.

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Deriving cloud information from all-sky images for intra-hour PV nowcasting

10.5194/egusphere-egu21-14726 ◽

2021 ◽

Author(s):

Philipp Gregor ◽

Tobias Zinner ◽

Bernhard Mayer ◽

Josef Schreder

Keyword(s):

Power Plant ◽

Radiative Transfer ◽

Power Plants ◽

Reference Data ◽

Weather Prediction ◽

Energy Output ◽

Cloud Base ◽

Transfer Model ◽

Weather Situation ◽

Cloud Base Height

Energy output from photovoltaics (PV) strongly depends on the respective weather situation. To ensure continuous energy availability in power grids with large PV contribution, flexibly manageable power plants have to compensate for variations in PV power production. Within the project NETFLEX, an intra-hour irradiance now-casting algorithm is developed as a basis for a PV power forecast used for management of a combined PV / biogas power plant.The now-casting algorithm is designed around a cloud representation in a simplistic 2D advection model, which is updated with currently measured data and which projects cloud situations up to 15 minutes into the future. Main input to the model are images captured by two CMS Schreder all-sky imagers (ASI) installed at the PV plant in locations separated by about 530m. Captured images are processed to extract cloud masks, cloud base heights and cloud movement. To obtain cloud masks, ratios of red and blue channels as well as saturation and brightness are compared to reference data from a clearsky library. This library is composed from synthetic clearsky data computed by the radiative transfer model libRadtran (Mayer and Kylling, 2005), which are processed to resemble imager geometry and optics. The creation of synthetic references allows for any desired sun position and aerosol condition. Simultaneously captured images of both cameras are evaluated and corresponding pixels are matched. Exact calibration of the imager geometry then allows for cloud base height derivation using the method of miss-pointing vectors (K&#246;lling et al., 2019). Consecutive images are evaluated for each ASI to estimate horizontal cloud motion by matching corresponding pixels. All cloud information computed from ASI images is assimilated into the 2D model as a base for cloud field predictions with information about cloud position, base height and velocity. The model-centered approach allows for flexible integration of additional data sources, e.g. satellite imagery and numerical weather prediction data.Validation of image evaluation methods and now-casting model is done using synthetic all-sky images of LES cloud fields. Additionally, cloud base height from a ceilometer as well as global and direct integrated solar irradiance were measured on site of the PV power plant. This also allows for validation on real world cases. Literature:K&#246;lling, T., Zinner, T., and Mayer, B.: Aircraft-based stereographic reconstruction of 3-D cloud geometry, Atmos. Meas. Tech., 12, 1155&#8211;1166, 2019.Mayer, B. and Kylling, A.: Technical note: The libRadtran software package for radiative transfer calculations - description and examples of use, Atmos. Chem. Phys., 5, 1855&#8211;1877, 2005.

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Assimilating All-Sky Himawari-8 Satellite Infrared Radiances: A Case of Typhoon Soudelor (2015)

Monthly Weather Review ◽

10.1175/mwr-d-16-0357.1 ◽

2018 ◽

Vol 146 (1) ◽

pp. 213-229 ◽

Cited By ~ 34

Author(s):

Takumi Honda ◽

Takemasa Miyoshi ◽

Guo-Yuan Lien ◽

Seiya Nishizawa ◽

Ryuji Yoshida ◽

...

Keyword(s):

High Resolution ◽

Tropical Cyclone ◽

Inner Core ◽

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Third Generation ◽

The Third ◽

Infrared Radiances ◽

First Time ◽

Full Disk

AbstractJapan’s new geostationary satellite Himawari-8, the first of a series of the third-generation geostationary meteorological satellites including GOES-16, has been operational since July 2015. Himawari-8 produces high-resolution observations with 16 frequency bands every 10 min for full disk, and every 2.5 min for local regions. This study aims to assimilate all-sky every-10-min infrared (IR) radiances from Himawari-8 with a regional numerical weather prediction model and to investigate its impact on real-world tropical cyclone (TC) analyses and forecasts for the first time. The results show that the assimilation of Himawari-8 IR radiances improves the analyzed TC structure in both inner-core and outer-rainband regions. The TC intensity forecasts are also improved due to Himawari-8 data because of the improved TC structure analysis.

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

Weather and Forecasting ◽

10.1175/waf-d-14-00052.1 ◽

2015 ◽

Vol 30 (2) ◽

pp. 486-497 ◽

Cited By ~ 5

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.

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ANALYSIS OF STORM CATEGORY AND COALESCENCE ACTIVITY : RELATIONSHIP TO THE DAILY MEAN CATCHMENT RAINFALL (CASES CLOUD SEEDING OPERATIONAL IN LARONA AND CITARUM CATCHMENT AREA)

Jurnal Sains & Teknologi Modifikasi Cuaca ◽

10.29122/jstmc.v13i1.2204 ◽

2012 ◽

Vol 13 (1) ◽

pp. 7

Author(s):

Untung Haryanto ◽

Dini Harsanti ◽

R. Djoko Goenawan ◽

Krisna Adithya

Keyword(s):

Catchment Area ◽

Inverse Correlation ◽

Activity Relationship ◽

Cloud Base ◽

Cloud Seeding ◽

The Mean ◽

Cloud Base Height ◽

Threshold Range

Cloud seeding operational has been conducted in Indonesia. In this study, the two cases operational were analysis, ie Larona (2005) and Citarum Catchment Area (2011). The coalescence activity during operational were analysed using archives of NOAAGFS model sounding and it also used to determined storm category and ICA. For this purpose, the number parameters for moderate threshold range on Raob-55 software were reduced and modified with more suitable range for Indonesia region. Result indicated that in Larona Catchment Area, the most of the storm having category weak to moderate with mean of storm category 67% and 21% respectively, the mean ICA value was -5.7. Relative larger weight of Weak and Medium category of storm were shown in Citarum ie 72% for weak and 18%, with the mean of ICA was -2.7%. As consequences, proportion of Medium together with Strong category for Larona is larger than Citarum,resulting larger amount mean catchment rainfall for Larona (17.1mm) compare to the Citarum (5.2 mm). The coalescence actifity in cloud during operation was effective for booth of two area, but varies due to the varies of cloud base height temperature and potential buoyancy (PB). The mean of CCL temperature in Larona was 20.30C since PB was 3.80C. This study also found that mean 500mb temperature (T) and mean rising parcel (TP) in Larona catchments was more warmer comparing to the Citarum is -4.00C and -0.150C for Larona , and -5.30C and -1.50C for Citarum. Base of the result it has been concluded that ICA has inverse correlation to the mean daily catchments rainfall, since together of Medium and Strong Storm Category has positive correlation.Modifikasi awan sudah operasional. Pada studi ini dilakukan analisis dua kasus operasional , yaitu operasional di DAS (Daerah Aliran Sungai) Larona (2005) dan DAS Citarum (2011). Aktifitas koalesensi selama operasional dianalisis menggunakan arsip data sounding NOAA-GFS yang digunakan untuk menentukan ICA (Index Coalescence Activity – indeks aktifitas koealesensi) dan Storm Category – kategori awan hujan). Bagi keperluan ini banyaknya parameter dan rentang kategori moderat pada perangkat lunak Raob -55 di dikurangi dan dimodifikasi dengan yang paling sesuai dengan kondisi daerah di Indonesia. Hasil analisis menunjukkan sebagian besar awan hujan yang ada memiliki kategori weak hingga moderat – yaitu 67% dan 21% di DAS Larona dengan rata-rata nilai ICA besarnya -5.7, sedangkan di DAS Citarum, nilai kategori ini lebih besar yaitu 72% dan 18% dengan nilai rata rata ICA adalah -2.7. Sebagai konsekuensinya adalah porsi awan hujan dengan kategori ini lebih banyak muncul di DAS Larona dibandingkan pada DAS Citarum dengan rata rata hujan masing masing 17.1mm di DAS Larona dan 5.2mm di DAS Citarum. Aktivitas koalesensi di kedua DAS ini sama-sama aktif, variasinya ditentukan oleh variasi ketinggian dasar awan konvektif (CCL) dan potensi daya apung awan (PB). Di DAS Larona CCL cukup hangat yaitu 20.30C , dengan nilai PB 3.80C. Pada sutudi ini juga di peroleh bahwa rata rata suhu dan suhu parsel paras 500mb pada DAS Larona lebih hangat (-4.00C dan -0.150C) dari pada di DAS Citarum (-5.30C dan -1.50C). Dari studi ini, disimpulkan bahwa ICA berkorelasi terbalik dengan curah hujan harian, dan berkorelasi positif dengan awan hujan berkategori “ sedang” dan “kuat” secara bersama-sama.

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Inter-technique validation of tropospheric slant total delays

Atmospheric Measurement Techniques ◽

10.5194/amt-10-2183-2017 ◽

2017 ◽

Vol 10 (6) ◽

pp. 2183-2208 ◽

Cited By ~ 30

Author(s):

Michal Kačmařík ◽

Jan Douša ◽

Galina Dick ◽

Florian Zus ◽

Hugues Brenot ◽

...

Keyword(s):

Ray Tracing ◽

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Global Navigation Satellite Systems ◽

Satellite Systems ◽

Systematic Effects ◽

The Mean ◽

Standard Deviations ◽

Global Navigation Satellite ◽

The Impact

Abstract. An extensive validation of line-of-sight tropospheric slant total delays (STD) from Global Navigation Satellite Systems (GNSS), ray tracing in numerical weather prediction model (NWM) fields and microwave water vapour radiometer (WVR) is presented. Ten GNSS reference stations, including collocated sites, and almost 2 months of data from 2013, including severe weather events were used for comparison. Seven institutions delivered their STDs based on GNSS observations processed using 5 software programs and 11 strategies enabling to compare rather different solutions and to assess the impact of several aspects of the processing strategy. STDs from NWM ray tracing came from three institutions using three different NWMs and ray-tracing software. Inter-techniques evaluations demonstrated a good mutual agreement of various GNSS STD solutions compared to NWM and WVR STDs. The mean bias among GNSS solutions not considering post-fit residuals in STDs was −0.6 mm for STDs scaled in the zenith direction and the mean standard deviation was 3.7 mm. Standard deviations of comparisons between GNSS and NWM ray-tracing solutions were typically 10 mm ± 2 mm (scaled in the zenith direction), depending on the NWM model and the GNSS station. Comparing GNSS versus WVR STDs reached standard deviations of 12 mm ± 2 mm also scaled in the zenith direction. Impacts of raw GNSS post-fit residuals and cleaned residuals on optimal reconstructing of GNSS STDs were evaluated at inter-technique comparison and for GNSS at collocated sites. The use of raw post-fit residuals is not generally recommended as they might contain strong systematic effects, as demonstrated in the case of station LDB0. Simplified STDs reconstructed only from estimated GNSS tropospheric parameters, i.e. without applying post-fit residuals, performed the best in all the comparisons; however, it obviously missed part of tropospheric signals due to non-linear temporal and spatial variations in the troposphere. Although the post-fit residuals cleaned of visible systematic errors generally showed a slightly worse performance, they contained significant tropospheric signal on top of the simplified model. They are thus recommended for the reconstruction of STDs, particularly during high variability in the troposphere. Cleaned residuals also showed a stable performance during ordinary days while containing promising information about the troposphere at low-elevation angles.

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Validation of wind measurements of two mesosphere–stratosphere–troposphere radars in northern Sweden and in Antarctica

Atmospheric Measurement Techniques ◽

10.5194/amt-14-2813-2021 ◽

2021 ◽

Vol 14 (4) ◽

pp. 2813-2825

Author(s):

Evgenia Belova ◽

Peter Voelger ◽

Sheila Kirkwood ◽

Susanna Hagelin ◽

Magnus Lindskog ◽

...

Keyword(s):

Weather Prediction ◽

Radar Data ◽

Numerical Weather Prediction Model ◽

Mean Difference ◽

Research Station ◽

Random Errors ◽

Meridional Winds ◽

Wind Measurements ◽

The Mean ◽

High Level

Abstract. Two atmospheric VHF radars: ESRAD (Esrange MST radar) located near Kiruna in the Swedish Arctic and MARA (Moveable Atmospheric Radar for Antarctica) at the Indian research station Maitri in Antarctica perform wind measurements in the troposphere and lower stratosphere on a regular basis. We compared horizontal winds at altitudes between about 0.5 and 14 km derived from the radar data using the full correlation analysis (FCA) technique with radiosonde observations and models. The comparison with 28 radiosondes launched from January 2017 to August 2019 showed that ESRAD underestimates the zonal and meridional winds by about 8 % and 25 %, respectively. This is likely caused by the receiver group arrangement used for the FCA together with a high level of non-white noise. A similar result was found when comparing with the regional numerical weather prediction model HARMONIE-AROME (Bengtsson et al., 2017) for the period September 2018–May 2019. The MARA winds were compared with winds from radiosondes for the period February–October 2014 (291 occasions). In contrast to ESRAD, there is no indication that MARA underestimates the winds compared to the sondes. The mean difference between the radar and radiosonde winds is close to zero for both zonal and meridional components. The comparison of MARA with the ECMWF ERA5 reanalysis for January–December 2019 reveals good agreement with the mean difference between 0.1 and −0.5 m/s depending on the component and season. The random errors in the wind components (standard deviations over all estimates in 1 h averages) are typically 2–3 m/s for both radars. Standard deviation of the differences between radars and sondes are 3–5 m/s.

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