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,” “moderate,” “severe,” or “extreme” ( Federal Aviation Administration 2012 , their Table 7-1-9]. Although formal definitions of these severity categories are provided in terms of normal accelerations or airspeed fluctuations, in practice they are both subjective (based on aircrew interpretation) and aircraft dependent, making them ill suited for providing reliable and consistent maps of atmospheric turbulence levels. To address these deficiencies, an in situ turbulence-reporting algorithm ( Cornman et al
,” “moderate,” “severe,” or “extreme” ( Federal Aviation Administration 2012 , their Table 7-1-9]. Although formal definitions of these severity categories are provided in terms of normal accelerations or airspeed fluctuations, in practice they are both subjective (based on aircrew interpretation) and aircraft dependent, making them ill suited for providing reliable and consistent maps of atmospheric turbulence levels. To address these deficiencies, an in situ turbulence-reporting algorithm ( Cornman et al
On the Performance of Airborne Meteorological Observations against Other In Situ Measurements1. Introduction Upper-air observations are critical for many aspects of operational meteorology. Forecasters, for example, rely on atmospheric profiles to assess atmospheric stability or the potential of hazardous precipitation types, while analyses of data assimilation (D/A) systems used in numerical weather prediction (NWP) models show that in situ profiles continue to play a prominent role in facilitating accurate forecast simulations ( Eyre and Reid 2014 ). Since their operational
1. Introduction Upper-air observations are critical for many aspects of operational meteorology. Forecasters, for example, rely on atmospheric profiles to assess atmospheric stability or the potential of hazardous precipitation types, while analyses of data assimilation (D/A) systems used in numerical weather prediction (NWP) models show that in situ profiles continue to play a prominent role in facilitating accurate forecast simulations ( Eyre and Reid 2014 ). Since their operational
oscillations over tropical landmasses, as documented in Schindelegger (2014) . Model discretizations and vertical domains may be revisited in a similar manner, taking into account that rigid-lid boundaries likely create spurious resonances of S 2 at the surface ( Hamilton et al. 2008 ). Assimilated samples of barometer recordings in each analysis cycle depart from an optimal spatiotemporal coverage, allowing the model physics or observations from other and probably less suited (non in situ) sensors to
oscillations over tropical landmasses, as documented in Schindelegger (2014) . Model discretizations and vertical domains may be revisited in a similar manner, taking into account that rigid-lid boundaries likely create spurious resonances of S 2 at the surface ( Hamilton et al. 2008 ). Assimilated samples of barometer recordings in each analysis cycle depart from an optimal spatiotemporal coverage, allowing the model physics or observations from other and probably less suited (non in situ) sensors to
studies have investigated the performance of MERRA-2 and ERA-Interim fields for a variety of applications. Some of the uncertainties result from systematic dependences such as inaccuracies in input measurements, residual diagnosis uncertainties, and imperfections of atmospheric transmittance simulations and of processing algorithms. Guan et al. (2018) used in situ airborne dropsonde observations to validate the MERRA-2 and ERA-Interim total water vapor transport; they pointed out that the reanalysis
studies have investigated the performance of MERRA-2 and ERA-Interim fields for a variety of applications. Some of the uncertainties result from systematic dependences such as inaccuracies in input measurements, residual diagnosis uncertainties, and imperfections of atmospheric transmittance simulations and of processing algorithms. Guan et al. (2018) used in situ airborne dropsonde observations to validate the MERRA-2 and ERA-Interim total water vapor transport; they pointed out that the reanalysis
dynamical) air–sea coupling. As a proof of concept, the analysis scheme is applied to the problem of assessing the impact of the in situ ocean and atmosphere observing networks on coupled short-range (up to 7 days) forecast skill scores. With this configuration, strongly coupled data assimilation is evaluated with respect to weakly coupled data assimilation for the two cases where either oceanic or atmospheric observations are assimilated. Experiments that simultaneously assimilate both observing
dynamical) air–sea coupling. As a proof of concept, the analysis scheme is applied to the problem of assessing the impact of the in situ ocean and atmosphere observing networks on coupled short-range (up to 7 days) forecast skill scores. With this configuration, strongly coupled data assimilation is evaluated with respect to weakly coupled data assimilation for the two cases where either oceanic or atmospheric observations are assimilated. Experiments that simultaneously assimilate both observing
closure in the atmospheric and oceanic water and energy balances. Unfortunately, heir data, which start with the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) in 2003, do not cover the period in this study. Our continuous in situ measurements in the SCS should make a contribution to future validations. With the construct of new atmospheric stations on the islands in the SCS, we will have long time series of meteorological observations to propose new algorithms or refine
closure in the atmospheric and oceanic water and energy balances. Unfortunately, heir data, which start with the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) in 2003, do not cover the period in this study. Our continuous in situ measurements in the SCS should make a contribution to future validations. With the construct of new atmospheric stations on the islands in the SCS, we will have long time series of meteorological observations to propose new algorithms or refine
The KLIWAS North Sea Climatology. Part I: Processing of the Atmospheric Datanot specifically created with high-resolution for any region. In essence, atmospheric reanalyses are not suited without limitation and further assessment as a reference dataset in the North Sea region. Few gridded reference datasets exist for regional analyses over land and are primarily based on in situ observations, such as the European daily high-resolution gridded dataset (E-OBS; Haylock et al. 2008 ; van den Besselaar et al. 2011 ). There are no such reference datasets of the marginal seas
not specifically created with high-resolution for any region. In essence, atmospheric reanalyses are not suited without limitation and further assessment as a reference dataset in the North Sea region. Few gridded reference datasets exist for regional analyses over land and are primarily based on in situ observations, such as the European daily high-resolution gridded dataset (E-OBS; Haylock et al. 2008 ; van den Besselaar et al. 2011 ). There are no such reference datasets of the marginal seas
Applying Automated Underway Ship Observations to Numerical Model Evaluationimproving numerical models can best be achieved using high-quality evaluation datasets. Herein we demonstrate the application of in situ observations collected by automated instrumentation on ships at sampling rates ≤5 min as a means to evaluate numerical model analyses. The focus is on physical oceanographic parameters (velocity, salinity, and sea temperature); however, the techniques demonstrated could be applied using atmospheric, chemical, or biological measurements from similar vessels. The use of
improving numerical models can best be achieved using high-quality evaluation datasets. Herein we demonstrate the application of in situ observations collected by automated instrumentation on ships at sampling rates ≤5 min as a means to evaluate numerical model analyses. The focus is on physical oceanographic parameters (velocity, salinity, and sea temperature); however, the techniques demonstrated could be applied using atmospheric, chemical, or biological measurements from similar vessels. The use of
observations supplemented by ship-based measurements during a series of AEROSE campaigns has been developed and maintained for the GOES-R validation over the tropical Atlantic Ocean. In this study, the state-of-the-art GOES-R ACM, ADP, and LAP algorithms have been run in the GOES-R AWG AIT framework. In particular, this work has contributed to the prelaunch validation of the GOES-R ABI LAP–retrieved atmospheric profiles by utilizing an ABI proxy dataset consisting of Meteosat-9 SEVIRI spectral band IR
observations supplemented by ship-based measurements during a series of AEROSE campaigns has been developed and maintained for the GOES-R validation over the tropical Atlantic Ocean. In this study, the state-of-the-art GOES-R ACM, ADP, and LAP algorithms have been run in the GOES-R AWG AIT framework. In particular, this work has contributed to the prelaunch validation of the GOES-R ABI LAP–retrieved atmospheric profiles by utilizing an ABI proxy dataset consisting of Meteosat-9 SEVIRI spectral band IR
flows on in situ atmospheric gas and aerosol measurements at PDM. The geographical context and datasets are described in section 2 . Section 3 presents the three detection methods developed and used in this study. The results are discussed in section 4 , and some conclusions are drawn and perspectives suggested in section 5 . 2. Site description and data a. Geographical context The P2OA is a research platform for atmospheric observations affiliated with the University of Toulouse. It is located
flows on in situ atmospheric gas and aerosol measurements at PDM. The geographical context and datasets are described in section 2 . Section 3 presents the three detection methods developed and used in this study. The results are discussed in section 4 , and some conclusions are drawn and perspectives suggested in section 5 . 2. Site description and data a. Geographical context The P2OA is a research platform for atmospheric observations affiliated with the University of Toulouse. It is located
