Combining ground-based microwave radiometer and the AROME
convective scale model through 1DVAR retrievals in complex
terrain: an Alpine Valley case study

Martinet, Pauline; Cimini, Domenico; Angelis, Francesco De; Canut, Guylaine; Unger, Vinciane; Tzanos, Diane; Paci, Alexandre

doi:10.5194/amt-2017-144

Cited by 4 publications

(6 citation statements)

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“…Brightness temperatures from MWR observations were also bias-corrected with a similar method as detailed in Martinet et al (2017Martinet et al ( , 2020. This method is based on a daily monitoring of differences between observed brightness temperatures and simulated brightness temperatures from the AROME model 1 h forecasts during clear-sky conditions only.…”

Section: Sofog-3d Field Campaignmentioning

confidence: 99%

“…For this reason, retrievals of temperature, humidity or LWC using cloud radars or MWRs typically employ further constraints. This can be done with physical parameterisations, for example, about the size distribution of hydrometeors or adiabaticity (Fox and Illingworth, 1997;Pospichal et al, 2012), variational retrieval methods that include additional information on an a priori estimate of the atmospheric state (Martinet et al, 2015(Martinet et al, , 2017, instrumental synergy (Matrosov et al, 1992;Crewell and Löhnert, 2003;Tinel et al, 2005), or a combination of these techniques (Löhnert et al, 2008;Che et al, 2016;Ebell et al, 2017;Turner and Löhnert, 2021).…”

Section: Introductionmentioning

confidence: 99%

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An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

et al. 2022

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Abstract. A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution numerical weather prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but are also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one-dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D- and 4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. This dataset was constructed by perturbing a high-resolution forecast dataset of fog and low-cloud cases by its expected errors. The algorithm is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than those found from the retrievals on the dataset of real observations. It was found that LWC can be retrieved in idealised conditions with an uncertainty of less than 0.04 g m−3. With real data, as expected, retrievals with a good correlation (0.7) to in situ measurements were found but with a higher uncertainty than the synthetic dataset of around 0.06 g m−3 (41 %). This was reduced to 0.05 g m−3 (35 %) when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging results for future improvement of the AROME model initial state during fog conditions.

show abstract

Section: Sofog-3d Field Campaignmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

et al. 2022

View full text Add to dashboard Cite

show abstract

Section: Sofog-3d Field Campaignmentioning

confidence: 99%

“…For this reason, retrievals of temperature, humidity or LWC using cloud radars or MWRs typically employ further constraints. This can be done with physical parameterisations-for example, about the size distribution of hydrometeors or adiabaticity- (Fox and Illingworth, 1997;Pospichal et al, 2012), variational retrieval methods which include additional information on an a priori estimate of the atmospheric state (Martinet et al, 2015(Martinet et al, , 2017, instrumental synergy (Matrosov et al, 1992;Crewell and Löhnert, 2003;Tinel et al, 2005) or a combination of these techniques (Löhnert et al, 2007;Che et al, 2016;Ebell et al, 2017;Turner and Löhnert, 2021).…”

Section: Introductionmentioning

confidence: 99%

An Optimal Estimation Algorithm for the Retrieval of Fog and Low Cloud Thermodynamic and Micro-physical Properties

Bell

Martinet

Caumont

et al. 2022

Preprint

Self Cite

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Abstract. A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution Numerical Weather Prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D/4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. It is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than that found from the retrievals on the dataset of real observations. It was found that retrieval of LWC can be obtained on idealised conditions with an uncertainty of less than 0.04 gm−3. With real data, as expected, retrievals with a good correlation (0.7) to in-situ measurements, but with a higher uncertainty than the synthetic dataset, of around 0.6 gm−3, was found. This was reduced to 0.5 gm−3 when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME model (Application of Research to Operations at MEsoscale) showing encouraging results for future improvement of the AROME model initial state during fog conditions.

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“…To improve retrieval results, Cimini et al () adopted the U.S. National Oceanic and Atmospheric Administration Local Analysis and Prediction System's hourly analysis as the background information in their 1DVAR retrieval for the 2010 Winter Olympics. Martinet et al () introduced temperature profile retrievals in an Alpine valley by merging brightness temperature measurements from the ground‐based MWR with 1‐hr forecasts from the convective scale model. Recently, NCEP has developed a new operational hourly updated numerical weather prediction system, the Rapid Refresh (RAP), covering all of North America (Benjamin et al, ) which allows for more accurate a priori information on the current atmospheric state.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Atmospheric Profiles in the New York State Mesonet Using One‐Dimensional Variational Algorithm

Yang

Min

2018

JGR Atmospheres

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A one‐dimensional variational (1DVAR) algorithm was developed, which combines measurements taken by the ground‐based microwave radiometer profiler (MWRP) with the Rapid Refresh (RAP) model to retrieve a more accurate temperature and humidity profiles under clear sky. The passive and active microwave‐vector radiative transfer model was used to simulate brightness temperatures and calculate weighting functions for 22 channels of the MWRP. As MWRP measurements are mainly weighted in the lower atmosphere, a measurement adjustment is performed to reduce the contribution from above 10 km. The results of the 1DVAR algorithm have been compared with the MWRP built‐in neural network (NN) retrievals and radiosonde observations, which show that the 1DVAR method outperforms the NN retrieval both in temperature and water vapor. Our statistical study further shows that the water vapor profiles from RAP are biased higher than the radiosonde observations, while the 1DVAR‐retrieved humidity values show significant improvements throughout the entire 10‐km range. The improvements in temperature profiles occur mainly within the lowest 4‐km atmosphere, while upper level retrievals are mostly influenced by initial values. This is consistent with the characteristics of the Jacobian matrix and the background error covariance matrix. The outcomes of 44 clear‐sky cases show that the maximum mean retrieval error of the 1DVAR algorithm is less than 0.2 K for temperature below 4 km and less than 0.15 g/m3 in water vapor density. These results are shown to be significantly better than the NN retrieval (3.0 K and 1.25 g/m3) and also superior to the RAP reanalysis (0.3 K and 0.5 g/m3).

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Combining ground-based microwave radiometer and the AROME convective scale model through 1DVAR retrievals in complex terrain: an Alpine Valley case study

Cited by 4 publications

References 0 publications

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

An Optimal Estimation Algorithm for the Retrieval of Fog and Low Cloud Thermodynamic and Micro-physical Properties

Retrieval of Atmospheric Profiles in the New York State Mesonet Using One‐Dimensional Variational Algorithm

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