Column water vapor determination in night period with a lunar photometer prototype

Barreto, África; Cuevas, E.; Damiri, Bahaiddin; Romero, P. M.; Almansa, Fernando

doi:10.5194/amt-6-2159-2013

Cited by 21 publications

(18 citation statements)

References 23 publications

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“…The ALADIN-HIRLAM cooperation is an international effort of 26 countries (mainly from Europe) to develop a mesoscale weather forecasting system. One configuration of the common ALADIN/HIRLAM NWP system, HAR-MONIE/AROME (Bengtsson et al, 2017), has been used operationally at FMI since 2006. HARMONIE/AROME is a non-hydrostatic model based on fully compressible Euler equations, where the time integration of the equation set is handled with a two-time-level semi-implicit semi-Lagrangian advection scheme.…”

Section: Model Data -Aladin/hirlam Nwp Systemmentioning

confidence: 99%

“…The model's physical parameterization package includes treatment of sub-grid scale processes related to cloud microphysics, turbulence, radiation transfer, shallow convection, surface and soil. All the parameterization schemes are described in detail by Bengtsson et al (2017) and Seity et al (2011).…”

Section: Model Data -Aladin/hirlam Nwp Systemmentioning

confidence: 99%

“…In the present paper, we calibrate Raman lidar WVMR profiles using in situ, satellite and model data. We have used RSs, retrievals from AIRS/AMSU instruments on board of Aqua satellite (Parkinson, 2003) and modeled WVMR from ALADIN/HIRLAM NWP model (Seity et al, 2011;Bengtsson et al, 2017). Our dataset concentrates on three field campaigns conducted during 2014 and 2015.…”

Section: Introductionmentioning

confidence: 99%

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Profiling water vapor mixing ratios in Finland by means of a Raman lidar, a satellite and a model

Filioglou¹,

Franck²,

Niemelä³

et al. 2017

Atmos. Meas. Tech.

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crease in water vapor mixing ratios is observed, except during summer months where favorable atmospheric conditions enable higher mixing ratio values at higher altitudes. Lastly, the seasonal change in disagreement between the lidar and the model has been studied. The analysis showed that, on average, the model underestimates water vapor mixing ratios at high altitudes during spring and summer.Published by Copernicus Publications on behalf of the European Geosciences Union.

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Section: Model Data -Aladin/hirlam Nwp Systemmentioning

confidence: 99%

Section: Model Data -Aladin/hirlam Nwp Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Profiling water vapor mixing ratios in Finland by means of a Raman lidar, a satellite and a model

Filioglou¹,

Franck²,

Niemelä³

et al. 2017

Atmos. Meas. Tech.

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“…Totems and Chazette (2016) presented a kitebased calibration method by using a meteorological probe at a flown kite over the water vapor lidar site. Additionally, cal-ibration methods based on WVMRs retrieved from satellite and model data (Parkinson, 2003;Seity et al, 2011;Bengtsson et al, 2017;Filioglou et al, 2017) were investigated.…”

Section: Introductionmentioning

confidence: 99%

Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data

et al. 2018

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We present a practical method to continuously calibrate Raman lidar observations of water vapor mixing ratio profiles. The water vapor profile measured with the multiwavelength polarization Raman lidar Polly XT is calibrated by means of co-located AErosol RObotic NETwork (AERONET) sun photometer observations and Global Data Assimilation System (GDAS) temperature and pressure profiles. This method is applied to lidar observations conducted during the Cyprus Cloud Aerosol and Rain Experiment (CyCARE) in Limassol, Cyprus. We use the GDAS temperature and pressure profiles to retrieve the water vapor density. In the next step, the precipitable water vapor from the lidar observations is used for the calibration of the lidar measurements with the sun photometer measurements. The retrieved calibrated water vapor mixing ratio from the lidar measurements has a relative uncertainty of 11 % in which the error is mainly caused by the error of the sun photometer measurements. During CyCARE, nine measurement cases with cloud-free and stable meteorological conditions are selected to calculate the precipitable water vapor from the lidar and the sun photometer observations. The ratio of these two precipitable water vapor values yields the water vapor calibration constant. The calibration constant for the Polly XT Raman lidar is 6.56 g kg −1 ± 0.72 g kg −1 (with a statistical uncertainty of 0.08 g kg −1 and an instrumental uncertainty of 0.72 g kg −1 ). To check the quality of the water vapor calibration, the water vapor mixing ratio profiles from the simultaneous nighttime observations with Raman lidar and Vaisala radiosonde sounding are compared. The correlation of the water vapor mixing ratios from these two instruments is determined by using all of the 19 simultaneous nighttime measurements during CyCARE. Excellent agreement with the slope of 1.01 and the R 2 of 0.99 is found. One example is presented to demonstrate the full potential of a well-calibrated Raman lidar. The relative humidity profiles from lidar, GDAS (simulation) and radiosonde are compared, too. It is found that the combination of water vapor mixing ratio and GDAS temperature profiles allow us to derive relative humidity profiles with the relative uncertainty of 10-20 %.

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“…There are numerous techniques for measuring IWV, both from ground and from space. Among ground-based measurements there are microwave radiometers (Turner et al, 2007), sun-photometers (Ichoku et al, 2002), lunar-photometers (Barreto et al, 2013), star-photometers (Pérez-Ramírez et al, 2012), Lidar (Turner et al, 2002), GPS system (Ortiz de Galisteo et al, 2011), and radio-sounding (Torres et al, 2010). Space measurements are performed using satellites which collect information from different parts of the electromagnetic spectrum: microwave (Jones et al, 2009), visible (Román et al, 2015;Wang et al, 2014), near-infra-red (Grossi et al, 2015) and infra-red (Bennouna et al, 2013).…”

mentioning

confidence: 99%

Inter-comparison of integrated water vapor from satellite instruments using reference GPS data at the Iberian Peninsula

Vaquero-Martínez

Antón

Galisteo

et al. 2018

Remote Sensing of Environment

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This paper focuses on the inter-comparison of integrated water vapor (IWV) products derived from the following satellite instruments: Global Ozone Monitoring Instrument (GOME-2), Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, Ozone Monitoring Instrument (OMI), Spining Enhanced Visible and InfraRed Imager (SEVIRI), Atmospheric Infrared Sounder (AIRS), and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY). IWV data from GPS in nine groundbased stations located in the Iberian Peninsula are used as reference. The study period extends from 2007 to 2012. The results show that, in general, OMI has good accuracy (pseudomedian of the relative differences between OMI and GPS IWV of (−0.7 ± 1.1)%). However, OMI, SCIAMACHY and AIRS show higher interquartile range (IQR) (which indicates lower precision) than the rest of satellite instruments. Both MODIS satellite instruments and SEVIRI products tend to slightly underestimate reference IWV data while GOME-2 exhibits a notable overestimation (16.7 ± 0.8%). All satellite instruments showed a tendency to reduce IWV extreme values: low IWV is overestimated while high IWV is underestimated. As for the influence of solar zenith angle (SZA), it can be observed that GOME-2 strongly overestimates the reference for high SZA values (by around 60% for SZA 60 − 80°). OMI shows, however, a high IQR for high SZA values. Both MODIS instruments show an increase in the pseudomedian of relative differences and IQR with SZA at daytime, with more stable values at night. Seasonal dependence is mainly due to the SZA and IWV typical values in each season. In general, in summer the tendency is to underestimate with low IQR (which happens when IWV is high and SZA is low), and in winter the trend is to overestimate with high IQR (which happens when IWV is low and SZA is high). SCIAMACHY shows a high pseudomedian in summer and autumn, and lower in winter and spring. It must be noted that GOME-2 shows a higher overestimation and OMI shows a higher IQR than other satellite instruments in winter and autumn. The influence of clouds was also studied, showing an increase of IQR as cloudiness increases in all satellites. Pseudomedian also worsens as cloudiness increases, generally. 1. Introduction Water vapor plays a crucial role in Earth's radiative balance, since it is the main absorber of the infrared radiation emitted from Earth's surface, and therefore responsible for air heating in the low layers. Regarding energy transport, water vapor's latent heat is a very effective mechanism. Water is evaporated at low latitudes, and water vapor is transported to higher latitudes where condensation releases high

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Column water vapor determination in night period with a lunar photometer prototype

Cited by 21 publications

References 23 publications

Profiling water vapor mixing ratios in Finland by means of a Raman lidar, a satellite and a model

Profiling water vapor mixing ratios in Finland by means of a Raman lidar, a satellite and a model

Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data

Inter-comparison of integrated water vapor from satellite instruments using reference GPS data at the Iberian Peninsula

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