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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
1. Introduction In recent years, multiple instruments capable of measuring sea surface temperature (SST) have been flown on satellites. It has been well established that high-quality global SSTs can be produced by blending SST data from multiple satellites and in situ observations ( Reynolds 1988 ; Reynolds and Smith 1994 ; Reynolds et al. 2005 ). Such global blended SSTs have played an important role in a number of climatological analyses and air–sea interaction studies. Motivated by the
1. Introduction In recent years, multiple instruments capable of measuring sea surface temperature (SST) have been flown on satellites. It has been well established that high-quality global SSTs can be produced by blending SST data from multiple satellites and in situ observations ( Reynolds 1988 ; Reynolds and Smith 1994 ; Reynolds et al. 2005 ). Such global blended SSTs have played an important role in a number of climatological analyses and air–sea interaction studies. Motivated by the
instantaneous retrieved snowfall rates of up to 100%–200% ( Wood 2011 ). These large uncertainties arise, in part, from the great variance in snow microphysical properties observed in nature (e.g., Mitchell 1996 , and references therein; Passarelli 1978 ) and in associated radar scattering properties. In response, Cooper et al. (2017 , hereafter C17 ), explored the use of in situ, event-specific observations of snow microphysical properties to constrain radar-based retrievals of snowfall. This work was
instantaneous retrieved snowfall rates of up to 100%–200% ( Wood 2011 ). These large uncertainties arise, in part, from the great variance in snow microphysical properties observed in nature (e.g., Mitchell 1996 , and references therein; Passarelli 1978 ) and in associated radar scattering properties. In response, Cooper et al. (2017 , hereafter C17 ), explored the use of in situ, event-specific observations of snow microphysical properties to constrain radar-based retrievals of snowfall. This work was
Evaluation of Satellite and Reanalysis Wind Products with In Situ Wave Glider Wind Observations in the Southern Oceansatellite-derived wind data (such as Yuan et al. 2009 ) to perform a validation prior to the start of their analysis in order to determine the ability of satellite-blended datasets to accurately represent wind fields at the air–sea interface and to determine whether postprocessing is required to reduce errors once compared to in situ measurements. There is a great need to reduce or eliminate this time costly step in wind data analysis. Further, the dearth of in situ wind stress observations in the
satellite-derived wind data (such as Yuan et al. 2009 ) to perform a validation prior to the start of their analysis in order to determine the ability of satellite-blended datasets to accurately represent wind fields at the air–sea interface and to determine whether postprocessing is required to reduce errors once compared to in situ measurements. There is a great need to reduce or eliminate this time costly step in wind data analysis. Further, the dearth of in situ wind stress observations in the
Combining In Situ and Satellite Observations to Retrieve Salinity and Density at the Ocean Surface; Buongiorno Nardelli et al. 2012 ; Mulet et al. 2012 ; Buongiorno Nardelli 2013 ; Pascual et al. 2015 ), as well as the tracking of water masses of different origins (e.g., Sabia et al. 2014 ). However, the low number of observations available has significantly limited the study of SSS and SSD variability. In fact, in situ measurements are very sparse and only with the advent of autonomous profilers could they provide an almost global coverage. In practice, even combining all available in situ data
; Buongiorno Nardelli et al. 2012 ; Mulet et al. 2012 ; Buongiorno Nardelli 2013 ; Pascual et al. 2015 ), as well as the tracking of water masses of different origins (e.g., Sabia et al. 2014 ). However, the low number of observations available has significantly limited the study of SSS and SSD variability. In fact, in situ measurements are very sparse and only with the advent of autonomous profilers could they provide an almost global coverage. In practice, even combining all available in situ data
. 2013 ). The representation of the LRR in ρ hv and Φ DP is inconsistent from case to case, and thus, it is not possible to identify a typical range of values. Its width usually ranges from ~300 m to ~1 km, and it is often most evident below 2.5–3.0 km ( Snyder et al. 2013 ). Fig . 1. PPI displays of (a) reflectivity (dB Z ) and (b) differential reflectivity (dB) observations that illustrate an LRR in an 18 May 2010 tornadic supercell, adapted from Snyder et al. (2013) . Arrows indicate the
. 2013 ). The representation of the LRR in ρ hv and Φ DP is inconsistent from case to case, and thus, it is not possible to identify a typical range of values. Its width usually ranges from ~300 m to ~1 km, and it is often most evident below 2.5–3.0 km ( Snyder et al. 2013 ). Fig . 1. PPI displays of (a) reflectivity (dB Z ) and (b) differential reflectivity (dB) observations that illustrate an LRR in an 18 May 2010 tornadic supercell, adapted from Snyder et al. (2013) . Arrows indicate the
; Dowell et al. 2005 ; Snyder and Bluestein 2014 ). In light of these uncertainties, direct observations of near-surface tornadic winds are highly desirable. Historically, in situ measurements of conditions within tornadoes [e.g., see Table 1 of Karstens et al. (2010) ] have largely been limited to pressure fluctuations obtained through chance encounters with fixed instruments, although wind data have also been obtained in a few such cases (e.g., Fujita 1970 ; Blanchard 2013 ; Kato et al. 2015
; Dowell et al. 2005 ; Snyder and Bluestein 2014 ). In light of these uncertainties, direct observations of near-surface tornadic winds are highly desirable. Historically, in situ measurements of conditions within tornadoes [e.g., see Table 1 of Karstens et al. (2010) ] have largely been limited to pressure fluctuations obtained through chance encounters with fixed instruments, although wind data have also been obtained in a few such cases (e.g., Fujita 1970 ; Blanchard 2013 ; Kato et al. 2015
weather stations which are simple to operate but expensive to maintain ( Dinku 2019 ). Most in situ observations are managed by National Hydrometeorological Services (NHMS) which have data sharing regulations that restrict open access to available records. Moreover, the number of observations by NHMS has dramatically declined in recent years, thus compounding the climate monitoring challenge ( van de Giesen et al. 2014 ). a. Availability of climate data for research in Africa The lack of adequate
weather stations which are simple to operate but expensive to maintain ( Dinku 2019 ). Most in situ observations are managed by National Hydrometeorological Services (NHMS) which have data sharing regulations that restrict open access to available records. Moreover, the number of observations by NHMS has dramatically declined in recent years, thus compounding the climate monitoring challenge ( van de Giesen et al. 2014 ). a. Availability of climate data for research in Africa The lack of adequate
that can be demonstrated for discharge. Places that are challenging for in situ monitoring are often ideal for remote sensing and vice versa. Remote sensing in conjunction with in situ observations, in a data assimilation framework, will provide the fullest picture of the state of our planet ( Neal et al. 2009 ). 2. Discussion River discharge is the most accurately measured component of the water cycle ( Hagemann and Dümenil 1998 ; Gutowski et al. 1997 ; Grabs et al. 1996 ) using traditional in
that can be demonstrated for discharge. Places that are challenging for in situ monitoring are often ideal for remote sensing and vice versa. Remote sensing in conjunction with in situ observations, in a data assimilation framework, will provide the fullest picture of the state of our planet ( Neal et al. 2009 ). 2. Discussion River discharge is the most accurately measured component of the water cycle ( Hagemann and Dümenil 1998 ; Gutowski et al. 1997 ; Grabs et al. 1996 ) using traditional in
. Section 3 analyzes the axisymmetric structural differences between TCs undergoing SEF and non-SEF TCs. Section 4 investigates the TC asymmetric structural evolution during SEF, and section 5 presents the conclusions from the current study. 2. Data and methodology a. Flight-level data and processing techniques The primary data source for this study is the Extended Flight-Level Dataset for Tropical Cyclones (FLIGHT+; Vigh et al. 2016 ). This dataset contains flight-level in situ observations of
. Section 3 analyzes the axisymmetric structural differences between TCs undergoing SEF and non-SEF TCs. Section 4 investigates the TC asymmetric structural evolution during SEF, and section 5 presents the conclusions from the current study. 2. Data and methodology a. Flight-level data and processing techniques The primary data source for this study is the Extended Flight-Level Dataset for Tropical Cyclones (FLIGHT+; Vigh et al. 2016 ). This dataset contains flight-level in situ observations of
