scholarly journals Accurate mobile remote sensing of XCO<sub>2</sub> and XCH<sub>4</sub> latitudinal transects from aboard a research vessel

2015 ◽  
Vol 8 (12) ◽  
pp. 5023-5038 ◽  
Author(s):  
F. Klappenbach ◽  
M. Bertleff ◽  
J. Kostinek ◽  
F. Hase ◽  
T. Blumenstock ◽  
...  
Keyword(s):  
Absorption Spectrometry ◽  
Total Carbon ◽  
Research Vessel ◽  
Weather Forecasts ◽  
Technical Developments ◽  
Solar Tracker ◽  
Network Instrument ◽  
Monitoring Service ◽  
Gosat Satellite ◽  
Better Than

Abstract. A portable Fourier transform spectrometer (FTS), model EM27/SUN, was deployed onboard the research vessel Polarstern to measure the column-average dry air mole fractions of carbon dioxide (XCO2) and methane (XCH4) by means of direct sunlight absorption spectrometry. We report on technical developments as well as data calibration and reduction measures required to achieve the targeted accuracy of fractions of a percent in retrieved XCO2 and XCH4 while operating the instrument under field conditions onboard the moving platform during a 6-week cruise on the Atlantic from Cape Town (South Africa, 34° S, 18° E; 5 March 2014) to Bremerhaven (Germany, 54° N, 19° E; 14 April 2014). We demonstrate that our solar tracker typically achieved a tracking precision of better than 0.05° toward the center of the sun throughout the ship cruise which facilitates accurate XCO2 and XCH4 retrievals even under harsh ambient wind conditions. We define several quality filters that screen spectra, e.g., when the field of view was partially obstructed by ship structures or when the lines-of-sight crossed the ship exhaust plume. The measurements in clean oceanic air, can be used to characterize a spurious air-mass dependency. After the campaign, deployment of the spectrometer alongside the TCCON (Total Carbon Column Observing Network) instrument at Karlsruhe, Germany, allowed for determining a calibration factor that makes the entire campaign record traceable to World Meteorological Organization (WMO) standards. Comparisons to observations of the GOSAT satellite and concentration fields modeled by the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) demonstrate that the observational setup is well suited to provide validation opportunities above the ocean and along interhemispheric transects.

scholarly journals Accurate mobile remote sensing of XCO<sub>2</sub> and XCH<sub>4</sub> latitudinal transects from aboard a research vessel

2015 ◽  
Vol 8 (7) ◽  
pp. 7413-7453 ◽  
Author(s):  
F. Klappenbach ◽  
M. Bertleff ◽  
J. Kostinek ◽  
F. Hase ◽  
T. Blumenstock ◽  
...  
Keyword(s):  
Absorption Spectrometry ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Research Vessel ◽  
Weather Forecasts ◽  
Technical Developments ◽  
Project Monitoring ◽  
Solar Tracker ◽  
Network Instrument ◽  
Gosat Satellite

Abstract. A portable Fourier Transform Spectrometer (FTS), model EM27/SUN, is deployed onboard the research vessel Polarstern to measure the column-average dry air mole fractions of carbon dioxide (XCO2) and methane (XCH4) by means of direct sunlight absorption spectrometry. We report on technical developments as well as data calibration and reduction measures required to achieve the targeted accuracy of fractions of a percent in retrieved XCO2 and XCH4 while operating the instrument under field conditions onboard the moving platform during a six week cruise through the Atlantic from Cape Town (South Africa, 34° S, 18° E) to Bremerhaven (Germany, 54° N, 19° E). We demonstrate that our solar tracker typically achieves a tracking precision of better than 0.05° toward the center of the sun throughout the ship cruise which facilitates accurate XCO2 and XCH4 retrievals even under harsh ambient wind conditions. We define several quality filters that screen spectra e.g. when the field-of-view is partially obstructed by ship structures or when the lines-of-sight cross the ship exhaust plume. The measurements in clean oceanic air, can be used to characterize a spurious airmass dependency. After the campaign, deployment of the spectrometer side-by-side the TCCON (Total Carbon Column Observing Network) instrument at Karlsruhe, Germany, allows for determining a calibration factor that makes the entire campaign record traceable to World Meteorological Organization (WMO) standards. Comparisons to observations of the GOSAT satellite and concentration fields modeled by the European Centre for Medium-Range Weather Forecasts (ECMWF) within the project Monitoring of Atmospheric Composition and Climate – Interim Implementation (MACC-II) demonstrate that the observational setup is well suited to provide validation opportunities above the ocean and along interhemispheric transects.

scholarly journals Visibility Prediction over South Korea Based on Random Forest

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 552
Author(s):  
Bu-Yo Kim ◽  
Joo Wan Cha ◽  
Ki-Ho Chang ◽  
Chulkyu Lee
Keyword(s):  
Random Forest ◽  
South Korea ◽  
Correlation Coefficients ◽  
Predictive Performance ◽  
Air Pollutant ◽  
Economic Losses ◽  
Observation Data ◽  
Local Data ◽  
Weather Forecasts ◽  
Monitoring Service

In this study, the visibility of South Korea was predicted (VISRF) using a random forest (RF) model based on ground observation data from the Automated Synoptic Observing System (ASOS) and air pollutant data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) model. Visibility was predicted and evaluated using a training set for the period 2017–2018 and a test set for 2019. VISRF results were compared and analyzed using visibility data from the ASOS (VISASOS) and the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) (VISLDAPS) operated by the Korea Meteorological Administration (KMA). Bias, root mean square error (RMSE), and correlation coefficients (R) for the VISASOS and VISLDAPS datasets were 3.67 km, 6.12 km, and 0.36, respectively, compared to 0.14 km, 2.84 km, and 0.81, respectively, for the VISASOS and VISRF datasets. Based on these comparisons, the applied RF model offers significantly better predictive performance and more accurate visibility data (VISRF) than the currently available VISLDAPS outputs. This modeling approach can be implemented by authorities to accurately estimate visibility and thereby reduce accidents, risks to public health, and economic losses, as well as inform on urban development policies and environmental regulations.

Aerosol Assimilation of lidar data from Satellite (AEOLUS) and Ground-based (EARLINET) instruments in COMPO-IFS.

2021 ◽  
Author(s):  
Julie Letertre-Danczak ◽  
Angela Benedetti ◽  
Drasko Vasiljevic ◽  
Alain Dabas ◽  
Thomas Flament ◽  
...  
Keyword(s):  
Quality Data ◽  
Lidar Data ◽  
Operational Model ◽  
Weather Forecasts ◽  
The Past ◽  
Medium Range ◽  
First Results ◽  
Monitoring Service ◽  
The Vertical Distribution ◽  
Good Quality Data

&lt;p&gt;Since several years, the number of aerosol data coming from lidar has grown and improved in quality. These new datasets are providing a valuable information on the vertical distribution of aerosols which is missing in the AOD (Aerosol Optical Depth), which has been used so far in aerosols analysis. The launch of AEOLUS in 2018 has increased the interest in the assimilation of the aerosol lidar information. In parallel, the ground-based network EARLINET (European Aerosol Research LIdar NETwork) has grown to cover the Europe with good quality data. Assimilation of these data in the ECMWF/CAMS (European Centre for Medium-range Weather Forecasts / Copernicus Atmosphere Monitoring Service) system is expected to provide improvements in the aerosol analyses and forecasts.&lt;br&gt;&lt;br&gt;Three preliminary studies have been done in the past four years using AEOLUS data (A3S-ESA funded) and EARLINET data (ACTRIS-2 and EUNADIC-AV, EU-funded). These studies have allowed the full development of the tangent linear and adjoint code for lidar backscatter in the ECMWF's 4D-VAR system. These developments are now in the operational model version in research mode. The first results are promising and open the path to more intake of aerosol lidar data for assimilation purposes. The future launch of EARTHCARE (Earth-Cloud Aerosol and Radiation Explorer) and later ACCP (Aerosol Cloud, Convention and Precipitation) might even upgrade the use of aerosol lidar data in COMPO-IFS (Composition-Integrated Forecast system).&lt;br&gt;&lt;br&gt;The most recent results using AEOLUS data (for October 2019 and April 2020) and using EARLINET data (October 2020) will be shown in this presentation. The output will be compared to the CAMS operational aerosol forecast as well as to independent data from AERONET (AErosol Robotic NETwork).&lt;/p&gt;

Artificial Measures Are Not Necessarily Better Than Natural Recovery for the Extremely Degraded Alpine Meadow: The Results of Simulated Degradation Restoration After Three Years

2021 ◽  
Vol 15 (2) ◽  
pp. 224-230
Author(s):  
Liuyan Tang ◽  
Lin Chen ◽  
Zhen’an Yang
Keyword(s):  
Microbial Biomass ◽  
Alpine Meadow ◽  
Total Carbon ◽  
Tibet Plateau ◽  
Microbial Biomass Nitrogen ◽  
Natural Recovery ◽  
Advantages And Disadvantages ◽  
Natural Treatment ◽  
Artificial Restoration ◽  
Better Than

Natural and artificial restoration measures are widely used to restore degraded ecosystems, such as degraded alpine meadow. The objective of this research was to evaluate the advantages and disadvantages of natural and artificial measures for extremely degraded alpine meadows. We removed the surface soil (0–10 cm) of the alpine meadow to simulate the extremely degraded “black soil beach,” and set artificial measures (planting Festuca sinensis (E) and Elymus sibircus L. cv. chuan-cao No. 1 (F)) and natural recovery (N) (without any artificial auxiliary measures) in the northeastern part of the Qinghai-Tibet Plateau (QTP), China. After 3 years, we determined the characteristics of community and soil in the artificial and natural treatment. The results show that the species number, above-and below-ground biomass (AB, BB), root-shoot ratio (R/S) in N is significantly higher than that in artificial restoration (E and F); while the community coverage and concentration of soil total carbon, total nitrogen, microbial biomass carbon, microbial biomass nitrogen and microbial biomass phosphorus (TC, TN, MBC, MBN and MBP) in artificial restoration is significantly higher than that in N. In conclusion, compared with N, artificial measures (E and F) are not completely beneficial to the development of plant community diversity and the restoration of soil nutrients in the extremely degraded meadow. Thus, the establishment of artificial grassland is not necessarily better than natural recovery for the extremely degraded alpine meadow.

scholarly journals Sensorless dual axis solar tracker using improved sun position algorithm

2020 ◽  
Vol 11 (3) ◽  
pp. 1305
Author(s):  
Chan Men Loon ◽  
Muhamad Zalani Daud
Keyword(s):  
Low Cost ◽  
Percentage Error ◽  
Total Output ◽  
Solar Pv ◽  
Pv Systems ◽  
Measured Angle ◽  
Solar Tracker ◽  
Fixed Panel ◽  
Fixed System ◽  
Better Than

This paper presents development of a prototype sensorless dual axis solar tracker for maximum extraction of solar energy. To prove the concept and evaluate the proposed algorithm, a low cost widely availabe materials were used which was programmed based on Arduino microcontroller. The porposed algorithm works based on two search methods namely the global search that approximates the best point location in a region, and local search that further determines the actual sun’s position. Experimental results showed that the proposed algorithm gives better performance compared to the existing sun position algorithm (SPA) - based method as well as the fixed panel system. In terms of total output power, the proposed algorithm gives 17.96% more efficient than the fixed system and 6.38% better than the SPA-based system. Furthermore, the percentage error of the experimental measured angle to the actual sun azimuth angle was relatively minimal (less than 3%) during clear day operation. The system was proven to be effective in tracking the sun for improved energy production of solar PV panels and the proposed algorithm also can be used for designing the tracker with larger size of solar PV systems.

Assessment of Temperature and Specific Humidity Inversions and Their Relationships in Three Global Reanalysis Products over the Arctic Ocean

2021 ◽  
Vol 60 (4) ◽  
pp. 493-511
Author(s):  
Liang Chang ◽  
Shiqiang Wen ◽  
Guoping Gao ◽  
Zhen Han ◽  
Guiping Feng ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Specific Humidity ◽  
The Arctic ◽  
Weather Forecasts ◽  
The Arctic Ocean ◽  
Temperature Inversions ◽  
Global Reanalysis ◽  
Less Common Multiple ◽  
Better Than

AbstractCharacteristics of temperature inversions (TIs) and specific humidity inversions (SHIs) and their relationships in three of the latest global reanalyses—the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-I), the Japanese 55-year Reanalysis (JRA-55), and the ERA5—are assessed against in situ radiosonde (RS) measurements from two expeditions over the Arctic Ocean. All reanalyses tend to detect many fewer TI and SHI occurrences, together with much less common multiple TIs and SHIs per profile than are seen in the RS data in summer 2008, winter 2015, and spring 2015. The reanalyses generally depict well the relationships among TI characteristics seen in RS data, except for the TIs below 400 m in summer, as well as above 1000 m in summer and winter. The depth is simulated worst by the reanalyses among the SHI characteristics, which may result from its sensitivity to the uncertainties in specific humidity in the reanalyses. The strongest TI per profile in RS data exhibits more robust dependency on surface conditions than the strongest SHI per profile, and the former is better presented by the reanalyses than the latter. Furthermore, all reanalyses have difficulties simulating the relationships between TIs and SHIs, together with the correlations between the simultaneous inversions. The accuracy and vertical resolution in the reanalyses are both important to properly capture occurrence and characteristics of the Arctic inversions. In general, ERA5 performs better than ERA-I and JRA-55 in depicting the relationships among the TIs. However, the representation of SHIs is more challenging than TIs in all reanalyses over the Arctic Ocean.

scholarly journals Evaluation of the Antarctic Circumpolar Wave Simulated by CMIP5 and CMIP6 Models

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 931
Author(s):  
Zhichao Lu ◽  
Tianbao Zhao ◽  
Weican Zhou
Keyword(s):  
Large Scale ◽  
Global Climate ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Weather Forecasts ◽  
Antarctic Circumpolar Wave ◽  
Sst Variability ◽  
The Antarctic ◽  
Chemistry Module ◽  
Better Than

As a coupled large-scale oceanic and atmospheric pattern in the Southern Ocean, the Antarctic circumpolar wave (ACW) has substantial impacts on the global climate. In this study, using the European Centre for Medium-Range Weather Forecasts ERA5 dataset and historical experiment outputs from 24 models of the Coupled Model Intercomparison Project Phase 5 and Phase 6 (CMIP5/CMIP6) spanning the 1980s and 1990s, the simulation capability of models for sea-level pressure (SLP) and sea surface temperature (SST) variability of the ACW is evaluated. It is shown that most models can capture well the 50-month period of the ACW. However, many simulations show a weak amplitude, but with various phase differences. Selected models can simulate SLP better than SST, and CMIP6 models generally perform better than the CMIP5 models. The best model for SLP simulation is the CanESM5 model from CMIP6, whereas the best model for SST simulation is the ACCESS1.3 model from CMIP5. It seems that the SST simulation benefits from the inclusion of both a carbon cycle process and a chemistry module, while the SLP simulation benefits from only the chemistry module. When both SLP and SST are taken into consideration, the CanESM5 model performs the best among all the selected models.

scholarly journals Ability of the 4-D-Var analysis of the GOSAT BESD XCO<sub>2</sub> retrievals to characterize atmospheric CO<sub>2</sub> at large and synoptic scales

2016 ◽  
Vol 16 (3) ◽  
pp. 1653-1671 ◽  
Author(s):  
Sébastien Massart ◽  
Anna Agustí-Panareda ◽  
Jens Heymann ◽  
Michael Buchwitz ◽  
Frédéric Chevallier ◽  
...  
Keyword(s):  
Optimal Estimation ◽  
Absolute Error ◽  
Total Carbon ◽  
Optical Absorption Spectroscopy ◽  
Weather Forecasts ◽  
Weather Patterns ◽  
Medium Range ◽  
Frontal Systems ◽  
Analysis System ◽  
Carbon Dioxide Co2

Abstract. This study presents results from the European Centre for Medium-Range Weather Forecasts (ECMWF) carbon dioxide (CO2) analysis system where the atmospheric CO2 is controlled through the assimilation of column-averaged dry-air mole fractions of CO2 (XCO2) from the Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to a free-run simulation (without assimilation of XCO2), and they are both evaluated against XCO2 data from the Total Carbon Column Observing Network (TCCON). We show that the assimilation of the GOSAT XCO2 product from the Bremen Optimal Estimation Differential Optical Absorption Spectroscopy (BESD) algorithm during the year 2013 provides XCO2 fields with an improved mean absolute error of 0.6 parts per million (ppm) and an improved station-to-station bias deviation of 0.7  ppm compared to the free run (1.1 and 1.4  ppm, respectively) and an improved estimated precision of 1  ppm compared to the GOSAT BESD data (3.3  ppm). We also show that the analysis has skill for synoptic situations in the vicinity of frontal systems, where the GOSAT retrievals are sparse due to cloud contamination. We finally computed the 10-day forecast from each analysis at 00:00  UTC, and we demonstrate that the CO2 forecast shows synoptic skill for the largest-scale weather patterns (of the order of 1000  km) even up to day 5 compared to its own analysis.

scholarly journals Remote sensing of CO<sub>2</sub> and CH<sub>4</sub> using solar absorption spectrometry with a low resolution spectrometer

2012 ◽  
Vol 5 (7) ◽  
pp. 1627-1635 ◽  
Author(s):  
C. Petri ◽  
T. Warneke ◽  
N. Jones ◽  
T. Ridder ◽  
J. Messerschmidt ◽  
...  
Keyword(s):  
High Resolution ◽  
A Priori ◽  
Absorption Spectrometry ◽  
Total Carbon ◽  
High Spectral Resolution ◽  
Low Resolution ◽  
Average Deviation ◽  
Solar Absorption ◽  
Fourier Transform Spectrometry ◽  
The Difference

Abstract. Throughout the last few years solar absorption Fourier Transform Spectrometry (FTS) has been further developed to measure the total columns of CO2 and CH4. The observations are performed at high spectral resolution, typically at 0.02 cm−1. The precision currently achieved is generally better than 0.25%. However, these high resolution instruments are quite large and need a dedicated room or container for installation. We performed these observations using a smaller commercial interferometer at its maximum possible resolution of 0.11 cm−1. The measurements have been performed at Bremen and have been compared to observations using our high resolution instrument also situated at the same location. The high resolution instrument has been successfully operated as part of the Total Carbon Column Observing Network (TCCON). The precision of the low resolution instrument is 0.32% for XCO2 and 0.46% for XCH4. A comparison of the measurements of both instruments yields an average deviation in the retrieved daily means of &amp;leq;0.2% for CO2. For CH4 an average bias between the instruments of 0.47% was observed. For test cases, spectra recorded by the high resolution instrument have been truncated to the resolution of 0.11 cm−1. This study gives an offset of 0.03% for CO2 and 0.26% for CH4. These results indicate that for CH4 more than 50% of the difference between the instruments results from the resolution dependent retrieval. We tentatively assign the offset to an incorrect a-priori concentration profile or the effect of interfering gases, which may not be treated correctly.

scholarly journals Generating Probabilistic Next-Day Severe Weather Forecasts from Convection-Allowing Ensembles Using Random Forests

2020 ◽  
Vol 35 (4) ◽  
pp. 1605-1631
Author(s):  
Eric D. Loken ◽  
Adam J. Clark ◽  
Christopher D. Karstens
Keyword(s):  
Random Forests ◽  
Weather Forecast ◽  
Severe Weather ◽  
Decision Makers ◽  
Limited Information ◽  
Weather Forecasts ◽  
Skill Scores ◽  
Weather Forecasters ◽  
Forecast Data ◽  
Better Than

AbstractExtracting explicit severe weather forecast guidance from convection-allowing ensembles (CAEs) is challenging since CAEs cannot directly simulate individual severe weather hazards. Currently, CAE-based severe weather probabilities must be inferred from one or more storm-related variables, which may require extensive calibration and/or contain limited information. Machine learning (ML) offers a way to obtain severe weather forecast probabilities from CAEs by relating CAE forecast variables to observed severe weather reports. This paper develops and verifies a random forest (RF)-based ML method for creating day 1 (1200–1200 UTC) severe weather hazard probabilities and categorical outlooks based on 0000 UTC Storm-Scale Ensemble of Opportunity (SSEO) forecast data and observed Storm Prediction Center (SPC) storm reports. RF forecast probabilities are compared against severe weather forecasts from calibrated SSEO 2–5-km updraft helicity (UH) forecasts and SPC convective outlooks issued at 0600 UTC. Continuous RF probabilities routinely have the highest Brier skill scores (BSSs), regardless of whether the forecasts are evaluated over the full domain or regional/seasonal subsets. Even when RF probabilities are truncated at the probability levels issued by the SPC, the RF forecasts often have BSSs better than or comparable to corresponding UH and SPC forecasts. Relative to the UH and SPC forecasts, the RF approach performs best for severe wind and hail prediction during the spring and summer (i.e., March–August). Overall, it is concluded that the RF method presented here provides skillful, reliable CAE-derived severe weather probabilities that may be useful to severe weather forecasters and decision-makers.

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