scholarly journals Ground based lidar and microwave radiometry synergy for high vertically resolved thermodynamic profiling

2015 ◽  
Vol 8 (5) ◽  
pp. 5467-5509
Author(s):  
M. Barrera-Verdejo ◽  
S. Crewell ◽  
U. Löhnert ◽  
E. Orlandi ◽  
P. Di Girolamo
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Degrees Of Freedom ◽  
Cloud Formation ◽  
Retrieval Algorithm ◽  
Vertical Resolution ◽  
Raman Lidar ◽  
Significant Information ◽  
Brightness Temperatures ◽  
Overlap Region

Abstract. Continuous monitoring of atmospheric humidity and temperature profiles is important for many applications, e.g. assessment of atmospheric stability and cloud formation. While lidar measurements can provide high vertical resolution albeit with limited coverage, microwave radiometers receive information throughout the troposphere though their vertical resolution is poor. In order to overcome these specific limitations the synergy of a Microwave Radiometer (MWR) and a Raman Lidar (RL) system is presented in this work. The retrieval algorithm that combines these two instruments is an Optimal Estimation Method (OEM) that allows for a uncertainty analysis of the retrieved profiles. The OEM combines measurements and a priori information taking the uncertainty of both into account. The measurement vector consists of a set of MWR brightness temperatures and RL water vapor profiles. The method is applied for a two month field campaign around Jülich, Germany for clear sky periods. Different experiments are performed to analyse the improvements achieved via the synergy compared to the individual retrievals. When applying the combined retrieval, on average the theoretically determined absolute humidity error can be reduced by 59.8% (37.9%) with respect to the retrieval using only-MWR (only-RL) data. The analysis in terms of degrees of freedom for signal reveals that most information is gained above the usable lidar range. The retrieved profiles are further evaluated using radiosounding and GPS water vapor measurements. Within a single case study we also explore the potential of the OEM for deriving the relative humidity profile, which is especially interesting to study cloud formation in the vicinity of cloud edges. To do so temperature information is added both from RL and MWR. For temperature, it is shown that the error is reduced by 47.1% (24.6%) with respect to the only-MWR (only-RL) profile. Due to the use of MWR brightness temperatures at multiple elevation angles, the MWR provides significant information below the lidar overlap region as shown by the degrees of freedom for signal. Therefore it might be sufficient to combine RL water vapor with multi-angle, multi-wavelength MWR for the retrieval of relative humidity, however, long-term studies are necessary in the future. In general, the benefit of the sensor combination is especially strong in regions where Raman Lidar data is not available (i.e. overlap region, poor signal to noise ratio), whereas if both instruments are available, RL dominates the retrieval.

scholarly journals Ground-based lidar and microwave radiometry synergy for high vertical resolution absolute humidity profiling

2016 ◽  
Vol 9 (8) ◽  
pp. 4013-4028 ◽  
Author(s):  
María Barrera-Verdejo ◽  
Susanne Crewell ◽  
Ulrich Löhnert ◽  
Emiliano Orlandi ◽  
Paolo Di Girolamo
Keyword(s):  
Water Vapor ◽  
Atmospheric Stability ◽  
Estimation Method ◽  
Absolute Humidity ◽  
Retrieval Algorithm ◽  
Vertical Resolution ◽  
Raman Lidar ◽  
Atmospheric Humidity ◽  
High Vertical Resolution ◽  
The Impact

Abstract. Continuous monitoring of atmospheric humidity profiles is important for many applications, e.g., assessment of atmospheric stability and cloud formation. Nowadays there are a wide variety of ground-based sensors for atmospheric humidity profiling. Unfortunately there is no single instrument able to provide a measurement with complete vertical coverage, high vertical and temporal resolution and good performance under all weather conditions, simultaneously. For example, Raman lidar (RL) measurements can provide water vapor with a high vertical resolution, albeit with limited vertical coverage, due to sunlight contamination and the presence of clouds. Microwave radiometers (MWRs) receive water vapor information throughout the troposphere, though their vertical resolution is poor. In this work, we present an MWR and RL system synergy, which aims to overcome the specific sensor limitations. The retrieval algorithm combining these two instruments is an optimal estimation method (OEM), which allows for an uncertainty analysis of the retrieved profiles. The OEM combines measurements and a priori information, taking the uncertainty of both into account. The measurement vector consists of a set of MWR brightness temperatures and RL water vapor profiles. The method is applied to a 2-month field campaign around Jülich (Germany), focusing on clear sky periods. Different experiments are performed to analyze the improvements achieved via the synergy compared to the individual retrievals. When applying the combined retrieval, on average the theoretically determined absolute humidity uncertainty is reduced above the last usable lidar range by a factor of  ∼  2 with respect to the case where only RL measurements are used. The analysis in terms of degrees of freedom per signal reveal that most information is gained above the usable lidar range, especially important during daytime when the lidar vertical coverage is limited. The retrieved profiles are further evaluated using radiosounding and Global Position Satellite (GPS) water vapor measurements. In general, the benefit of the sensor combination is especially strong in regions where Raman lidar data are not available (i.e., blind regions, regions characterized by low signal-to-noise ratio), whereas if both instruments are available, RL dominates the retrieval. In the future, the method will be extended to cloudy conditions, when the impact of the MWR becomes stronger.

scholarly journals Ground Based Lidar and Microwave Radiometry Synergy for High Vertical Resolution Absolute Humidity Profiling

2016 ◽  
Author(s):  
María Barrera-Verdejo ◽  
Susanne Crewell ◽  
Ulrich Löhnert ◽  
Emiliano Orlandi ◽  
Paolo Di Girolamo
Keyword(s):  
Water Vapor ◽  
Atmospheric Stability ◽  
Estimation Method ◽  
Absolute Humidity ◽  
Retrieval Algorithm ◽  
Vertical Resolution ◽  
Raman Lidar ◽  
Atmospheric Humidity ◽  
High Vertical Resolution ◽  
The Impact

Abstract. Continuous monitoring of atmospheric humidity profiles is important for many applications, e.g. assessment of atmospheric stability and cloud formation. Nowadays there is a wide variety of ground-based sensors for atmospheric humidity profiling. Unfortunately there is no single instrument able to provide a measurement with complete vertical coverage, high vertical and temporal resolution, and good performance under all weather conditions, simultaneously. For example, Raman lidar (RL) measurements can provide water vapor with a high vertical resolution albeit with limited vertical coverage, due to sunlight contamination and the presence of clouds. Microwave radiometers (MWR) receive water vapor information throughout the troposphere though their vertical resolution is poor. In this work, we present a MWR and RL system synergy, which aims to overcome the specific sensor limitations. The retrieval algorithm combining these two instruments is an Optimal Estimation Method (OEM), which allows for an uncertainty analysis of the retrieved profiles. The OEM combines measurements and a priori information taking the uncertainty of both into account. The measurement vector consists of a set of MWR brightness temperatures and RL water vapor profiles. The method is applied to a two month field campaign around Jülich (Germany), focusing on clear sky periods. Different experiments are performed to analyse the improvements achieved via the synergy compared to the individual retrievals. When applying the combined retrieval, on average the theoretically determined absolute humidity uncertainty can be reduced by 60 % (38 %) with respect to the retrieval using only-MWR (only-RL) data. The analysis in terms of degrees of freedom per signal reveals that most information is gained above the usable lidar range, especially important during daytime when the lidar vertical coverage is limited. The retrieved profiles are further evaluated using radiosounding and GPS water vapor measurements. In general, the benefit of the sensor combination is especially strong in regions where Raman lidar data is not available (i.e. blind region, regions characterized by low signal to noise ratio), whereas if both instruments are available, RL dominates the retrieval. In the future, the method will be extended to cloudy conditions, when the impact of the MWR becomes stronger.

scholarly journals A Satellite-Based Assessment of Upper-Tropospheric Water Vapor Measurements during AFWEX

2009 ◽  
Vol 48 (11) ◽  
pp. 2284-2294 ◽  
Author(s):  
Eui-Seok Chung ◽  
Brian J. Soden
Keyword(s):  
Water Vapor ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Raman Lidar ◽  
Test Bed ◽  
Upper Troposphere ◽  
Brightness Temperatures ◽  
Radiation Test ◽  
Humidity Measurements ◽  
Atmospheric Radiation Measurement

Abstract Consistency of upper-tropospheric water vapor measurements from a variety of state-of-the-art instruments was assessed using collocated Geostationary Operational Environmental Satellite-8 (GOES-8) 6.7-μm brightness temperatures as a common benchmark during the Atmospheric Radiation Measurement Program (ARM) First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Water Vapor Experiment (AFWEX). To avoid uncertainties associated with the inversion of satellite-measured radiances into water vapor quantity, profiles of temperature and humidity observed from in situ, ground-based, and airborne instruments are inserted into a radiative transfer model to simulate the brightness temperature that the GOES-8 would have observed under those conditions (i.e., profile-to-radiance approach). Comparisons showed that Vaisala RS80-H radiosondes and Meteolabor Snow White chilled-mirror dewpoint hygrometers are systemically drier in the upper troposphere by ∼30%–40% relative to the GOES-8 measured upper-tropospheric humidity (UTH). By contrast, two ground-based Raman lidars (Cloud and Radiation Test Bed Raman lidar and scanning Raman lidar) and one airborne differential absorption lidar agree to within 10% of the GOES-8 measured UTH. These results indicate that upper-tropospheric water vapor can be monitored by these lidars and well-calibrated, stable geostationary satellites with an uncertainty of less than 10%, and that correction procedures are required to rectify the inherent deficiencies of humidity measurements in the upper troposphere from these radiosondes.

scholarly journals Spaceborne differential absorption radar water vapor retrieval capabilities in tropical and subtropical boundary layer cloud regimes

2021 ◽  
Vol 14 (10) ◽  
pp. 6443-6468
Author(s):  
Richard J. Roy ◽  
Matthew Lebsock ◽  
Marcin J. Kurowski
Keyword(s):  
Boundary Layer ◽  
Water Vapor ◽  
Trade Wind ◽  
Retrieval Algorithm ◽  
Line Center ◽  
Vertical Resolution ◽  
Horizontal Integration ◽  
Differential Absorption ◽  
Near Surface ◽  
Cloud Regimes

Abstract. Differential absorption radar (DAR) near the 183 GHz water vapor absorption line is an emerging measurement technique for humidity profiling inside of clouds and precipitation with high vertical resolution, as well as for measuring integrated water vapor (IWV) in clear-air regions. For radar transmit frequencies on the water line flank away from the highly attenuating line center, the DAR system becomes most sensitive to water vapor in the planetary boundary layer (PBL), which is a region of the atmosphere that is poorly resolved in the vertical by existing spaceborne humidity and temperature profiling instruments. In this work, we present a high-fidelity, end-to-end simulation framework for notional spaceborne DAR instruments that feature realistically achievable radar performance metrics and apply this simulator to assess DAR's PBL humidity observation capabilities. Both the assumed instrument parameters and radar retrieval algorithm leverage recent technology and algorithm development for an existing airborne DAR instrument. To showcase the capabilities of DAR for humidity observations in a variety of relevant PBL settings, we implement the instrument simulator in the context of large eddy simulations (LESs) of five different cloud regimes throughout the trade-wind subtropical-to-tropical cloud transition. Three distinct DAR humidity observations are investigated: IWV between the top of the atmosphere and the first detected cloud bin or Earth's surface; in-cloud water vapor profiles with 200 meter vertical resolution; and IWV between the last detected cloud bin and the Earth's surface, which can provide a precise measurement of the sub-cloud humidity. We provide a thorough assessment of the systematic and random errors for all three measurement products for each LES case and analyze the humidity precision scaling with along-track measurement integration. While retrieval performance depends greatly on the specific cloud regime, we find generally that for a radar with cross-track scanning capability, in-cloud profiles with 200 m vertical resolution and 10 %–20 % uncertainty can be retrieved for horizontal integration distances of 100–200 km. Furthermore, column IWV can be retrieved with 10 % uncertainty for 10–20 km of horizontal integration. Finally, we provide some example science applications of the simulated DAR observations, including estimating near-surface relative humidity using the cloud-to-surface column IWV and inferring in-cloud temperature profiles from the DAR water vapor profiles by assuming a fully saturated environment.

Simultaneous Measurements of Atmospheric Water Vapor with MIR, Raman Lidar, and Rawinsondes

1995 ◽  
Vol 34 (7) ◽  
pp. 1595-1607 ◽  
Author(s):  
J. R. Wang ◽  
S. H. Melfi ◽  
P. Racette ◽  
D. N. Whitemen ◽  
L. A. Chang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Atmospheric Water Vapor ◽  
Raman Lidar ◽  
Atmospheric Water ◽  
Millimeter Wave Imaging ◽  
Brightness Temperatures ◽  
Simultaneous Measurements ◽  
Wave Imaging ◽  
Low Sensitivity ◽  
Measured Water

Abstract Simultaneous measurements of atmospheric water vapor were made by the Millimeter-wave Imaging Radiometer (MIR), Raman lidar, and rawinsondes. Two types of rawinsonde sensor packages (AIR and Vaisala) were carried by the same balloon. The measured water vapor profiles from Raman lidar, and the Vaisala and AIR sondes were used in the radiative transfer calculations. The calculated brightness temperatures were compared with those measured from the MIR at all six frequencies (89, 150, 183.3 ± 1, 183.3 ±3, 183.3 ±7, and 220 GHz). The results show that the MIR-measured brightness temperatures agree well (within ±K) with those calculated from the Raman lidar and Vaisala measurements. The brightness temperatures calculated from the AIR sondes differ from the MIR measurements by as much as 10 K, which can be attributed to low sensitivity of the AIR sondes at relative humidity less than 20%. Both calculated and the MIR-measured brightness temperatures were also used to retrieve water vapor profiles. These retrieved profiles were compared with those measured by the Raman lidar and rawinsondes. The results of these comparisons suggest that the MIR can measure the brightness of a target to an accuracy of at most ±K and is capable of retrieving useful water vapor profiles.

scholarly journals A Raman lidar at Maïdo Observatory (Reunion Island) to measure water vapor in the troposphere and lower stratosphere: calibration and validation

2017 ◽  
Author(s):  
Hélène Vérèmes ◽  
Guillaume Payen ◽  
Philippe Keckhut ◽  
Valentin Duflot ◽  
Jean-Luc Baray ◽  
...  
Keyword(s):  
Water Vapor ◽  
Lower Stratosphere ◽  
Calibration Coefficient ◽  
Integration Time ◽  
Relative Standard Error ◽  
Reunion Island ◽  
Vertical Resolution ◽  
Raman Lidar ◽  
Relative Standard ◽  
Réunion Island

Abstract. The Maïdo high-altitude observatory located in Reunion Island (21° S, 55.5° E) is equipped with Lidar1200, an innovative Raman lidar designed to measure the water vapor mixing ratio in the troposphere and the lower stratosphere. The calibration methodology is based on a GNSS (Global Navigation Satellite System) IWV (Integrated Water Vapor) dataset and lamp measurements. The mean relative standard error on the calibration coefficient is around 2.7 %. Two years of lidar water vapor measurements from November 2013 to October 2015 are now processed. By comparing CFH (Cryogenic Frost point Hygrometer) radiosonde profiles with the Raman lidar profiles, the ability of the lidar to provide accurate measurements is possible up to 22 km. The ability of measuring water vapor mixing ratios of a few ppmv in the lower stratosphere is demonstrated with a 48-hours integration time period, an absolute error lower than 0.8 ppmv and a relative error less than 20 %. This Raman lidar is dedicated to provide regular profiles of water vapor measurements with a high vertical resolution and low uncertainties to international networks; in the wider interest of research on stratosphere-troposphere exchange processes and on the long-term survey of water vapor in the upper troposphere and lower stratosphere in the Southern Hemisphere. A strategy of data sampling and filtering is proposed to meet these objectives with regard to the altitude range requested. 10-min time integration and 65–90 m vertical resolution ensure a vertical profile reaching 10 km, but more than 2800 minutes and a vertical resolution of 150–1300 m are necessary to reach the lower stratosphere with an uncertainty less than 20 %.

scholarly journals Potential of Dual-Frequency Radar and Microwave Radiometer Synergy for Water Vapor Profiling in the Cloudy Trade Wind Environment

2020 ◽  
Vol 37 (11) ◽  
pp. 1973-1986
Author(s):  
Sabrina Schnitt ◽  
Ulrich Löhnert ◽  
René Preusker
Keyword(s):  
Water Vapor ◽  
Information Content ◽  
Degrees Of Freedom ◽  
Microwave Radiometer ◽  
Optimal Estimation ◽  
Cloud Layer ◽  
Trade Wind ◽  
Dual Frequency ◽  
Shallow Convection ◽  
Brightness Temperatures

AbstractHigh-resolution boundary layer water vapor profile observations are essential for understanding the interplay between shallow convection, cloudiness, and climate in the trade wind atmosphere. As current observation techniques can be limited by low spatial or temporal resolution, the synergistic benefit of combining ground-based microwave radiometer (MWR) and dual-frequency radar is investigated by analyzing the retrieval information content and uncertainty. Synthetic MWR brightness temperatures, as well as simulated dual-wavelength ratios of two radar frequencies are generated for a combination of Ka and W band (KaW), as well as differential absorption radar (DAR) G-band frequencies (167 and 174.8 GHz, G2). The synergy analysis is based on an optimal estimation scheme by varying the configuration of the observation vector. Combining MWR and KaW only marginally increases the retrieval information content. The synergy of MWR with G2 radar is more beneficial due to increasing degrees of freedom (4.5), decreasing retrieval errors, and a more realistic retrieved profile within the cloud layer. The information and profile below and within the cloud is driven by the radar observations, whereas the synergistic benefit is largest above the cloud layer, where information content is enhanced compared to an MWR-only or DAR-only setup. For full synergistic benefits, however, G-band radar sensitivities need to allow full-cloud profiling; in this case, the results suggest that a combined retrieval of MWR and G-band DAR can help close the observational gap of current techniques.

scholarly journals Water Vapor Measurements by Howard University Raman Lidar during the WAVES 2006 Campaign

2010 ◽  
Vol 27 (1) ◽  
pp. 42-60 ◽  
Author(s):  
M. Adam ◽  
B. B. Demoz ◽  
D. D. Venable ◽  
E. Joseph ◽  
R. Connell ◽  
...  
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Relative Difference ◽  
Mixing Ratio ◽  
State Variables ◽  
Raman Lidar ◽  
Ensemble Averaging ◽  
Howard University ◽  
Validation Experiment ◽  
Water Vapor Mixing Ratio

Abstract Water vapor mixing ratio retrieval using the Howard University Raman lidar is presented with emphasis on three aspects: (i) comparison of the lidar with collocated radiosondes and Raman lidar, (ii) investigation of the relationship between atmospheric state variables and the relative performance of the lidar and sonde (in particular, their poor agreement), and (iii) comparison with satellite-based measurements. The measurements were acquired during the Water Vapor Validation Experiment Sondes/Satellites 2006 campaign. Ensemble averaging of water vapor mixing ratio data from 10 nighttime comparisons with Vaisala RS92 radiosondes shows, on average, an agreement within ±10%, up to ∼8 km. A similar analysis of lidar-to-lidar data of over 700 profiles revealed an agreement to within 20% over the first 7 km (10% below 4 km). A grid analysis, defined in the temperature–relative humidity space, was developed to characterize the lidar–radiosonde agreement and quantitatively localizes regions of strong and weak correlations as a function of altitude, temperature, or relative humidity. Three main regions of weak correlation emerge: (i) regions of low relative humidity and low temperature, (ii) regions of moderate relative humidity at low temperatures, and (iii) regions of low relative humidity at moderate temperatures. Comparison of Atmospheric Infrared Sounder and Tropospheric Emission Sounder satellite retrievals of moisture with those of Howard University Raman lidar showed a general agreement in the trend, but the satellites miss details in atmospheric structure because of their low resolution. A relative difference of about ±20% is usually found between lidar and satellite measurements for the coincidences available.

scholarly journals Multiparameter Raman Lidar Measurements for the Characterization of a Dry Stratospheric Intrusion Event

2009 ◽  
Vol 26 (9) ◽  
pp. 1742-1762 ◽  
Author(s):  
Paolo Di Girolamo ◽  
Donato Summa ◽  
Rossella Ferretti
Keyword(s):  
Water Vapor ◽  
High Resolution ◽  
Relative Humidity ◽  
Potential Temperature ◽  
Atmospheric Temperature ◽  
Deep Penetration ◽  
Raman Lidar ◽  
Dry Air ◽  
Stratospheric Intrusion ◽  
Lidar Measurements

Abstract The University of Basilicata Raman lidar system (BASIL) is operational in Potenza, Italy, and it is capable of performing high-resolution and accurate measurements of atmospheric temperature and water vapor based on the application of the rotational and vibrational Raman lidar techniques in the ultraviolet region. BASIL was recently involved in the 2005 International Lindenberg campaign for Assessment of Humidity and Cloud Profiling Systems and Its Impact on High-Resolution Modeling (LAUNCH 2005) experiment held from 12 September to 31 October 2005. A thorough description of the technical characteristics, measurement capabilities, and performances of BASIL is given in this paper. Measurements were continuously run between 1 and 3 October 2005, covering a dry stratospheric intrusion episode associated with a tropopause folding event. The measurements in this paper represent the first simultaneous Raman lidar measurements of atmospheric temperature, water vapor mixing ratio, and thus relative humidity reported for an extensive observation period (32 h). The use of water vapor to trace intruded stratospheric air allows the clear identification of a dry structure (∼1 km thick) originating in the stratosphere and descending in the free troposphere down to ∼3 km. A similar feature is present in the temperature field, with lower temperature values detected within the dry-air tongue. Relative humidity measurements reveal values as small as 0.5%–1% within the intruded air. The stratospheric origin of the observed dry layer has been verified by the application of a Lagrangian trajectory model. The subsidence of the intruding heavy dry air may be responsible for the gravity wave activity observed beneath the dry layer. Lidar measurements have been compared with the output of both the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and the European Centre for Medium-Range Weather Forecasts (ECMWF) global model. Comparisons in terms of water vapor reveal the capability of MM5 to reproduce the dynamical structures associated with the stratospheric intrusion episode and to simulate the deep penetration into the troposphere of the dry intruded layer. Moreover, lidar measurements of potential temperature are compared with MM5 output, whereas potential vorticities from both the ECMWF model and MM5 are compared with estimates obtained combining MM5 model vorticity and lidar measurements of potential temperature.

scholarly journals HCOOH measurements from space: TES retrieval algorithm and observed global distribution

2014 ◽  
Vol 7 (7) ◽  
pp. 2297-2311 ◽  
Author(s):  
K. E. Cady-Pereira ◽  
S. Chaliyakunnel ◽  
M. W. Shephard ◽  
D. B. Millet ◽  
M. Luo ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Transport Model ◽  
A Priori ◽  
Optimal Estimation ◽  
Initial Guess ◽  
Global Distribution ◽  
Retrieval Algorithm ◽  
Vertical Resolution ◽  
Chemical Transport Model ◽  
Thermal Contrast

Abstract. Presented is a detailed description of the TES (Tropospheric Emission Spectrometer)-Aura satellite formic acid (HCOOH) retrieval algorithm and initial results quantifying the global distribution of tropospheric HCOOH. The retrieval strategy, including the optimal estimation methodology, spectral microwindows, a priori constraints, and initial guess information, are provided. A comprehensive error and sensitivity analysis is performed in order to characterize the retrieval performance, degrees of freedom for signal, vertical resolution, and limits of detection. These results show that the TES HCOOH retrievals (i) typically provide at best 1.0 pieces of information; (ii) have the most vertical sensitivity in the range from 900 to 600 hPa with ~ 2 km vertical resolution; (iii) require at least 0.5 ppbv (parts per billion by volume) of HCOOH for detection if thermal contrast is greater than 5 K, and higher concentrations as thermal contrast decreases; and (iv) based on an ensemble of simulated retrievals, are unbiased with a standard deviation of ±0.4 ppbv. The relative spatial distribution of tropospheric HCOOH derived from TES and its associated seasonality are broadly correlated with predictions from a state-of-the-science chemical transport model (GEOS-Chem CTM). However, TES HCOOH is generally higher than is predicted by GEOS-Chem, and this is in agreement with recent work pointing to a large missing source of atmospheric HCOOH. The model bias is especially pronounced in summertime and over biomass burning regions, implicating biogenic emissions and fires as key sources of the missing atmospheric HCOOH in the model.

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