Estimating error variances of a microwave sensor and dropsondes aboard the Global Hawk in hurricanes using the three-cornered hat method

2020 ◽  
Author(s):  
Andrew C. Kren ◽  
Richard A. Anthes
Keyword(s):  
Relative Humidity ◽  
Integrated Circuit ◽  
Lower Troposphere ◽  
Specific Humidity ◽  
Data Sets ◽  
Method Error ◽  
Upper Troposphere ◽  
Spatial And Temporal Scales ◽  
Corrected Data ◽  
Global Hawk

AbstractThis study estimates the random error variances and standard deviations (STDs) for four data sets: Global Hawk (GH) dropsondes (DROP), the High-Altitude Monolithic Microwave Integrated Circuit Sounding Radiometer (HAMSR) aboard the GH, the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis, and the Hurricane Weather Research and Forecasting (HWRF) model, using the three-cornered hat (3CH) method. These estimates are made during the 2016 Sensing Hazards with Operational Unmanned Technology (SHOUT) season in the environment of four tropical cyclones from August to October. For temperature and specific and relative humidity, the ERA5, HWRF, and DROP data sets all have similar magnitudes of errors, with ERA5 having the smallest. The error STDs of temperature and specific humidity are less than 0.8 K and 1.0 g kg-1 over most of the troposphere, while relative humidity error STDs increase from less than 5% near the surface to between 10 and 20% in the upper troposphere. The HAMSR bias-corrected data have larger errors, with estimated error STDs of temperature and specific humidity in the lower troposphere between 1.5 and 2.0 K and 1.5 and 2.5 g kg-1. HAMSR’s relative humidity error STD increases from approximately 10% in the lower troposphere to 30% in the upper troposphere. The 3CH method error estimates are generally consistent with prior independent estimates of errors and uncertainties for the HAMSR and dropsonde data sets, although they are somewhat larger, likely due to the inclusion of representativeness errors (differences associated with different spatial and temporal scales represented by the data).

scholarly journals Nocturnal Relative Humidity Maxima above the Boundary Layer in the U.S. Midwest: A Diagnostic for the Mountain–Plains Solenoidal Circulation

2018 ◽  
Vol 146 (2) ◽  
pp. 641-658
Author(s):  
Amanda Mercer ◽  
Rachel Chang ◽  
Ian Folkins
Keyword(s):  
Relative Humidity ◽  
Cross Sections ◽  
Lower Troposphere ◽  
Vertical Motion ◽  
Specific Humidity ◽  
Reanalysis Dataset ◽  
Low Level Jet ◽  
Vertical Winds ◽  
Modern Era ◽  
The U.S

Measurements from the Aircraft Communications, Addressing, and Reporting System (ACARS) dataset between 2005 and 2014 are used to construct diurnal vertical cross sections of relative humidity in the lower troposphere at six airports in the U.S. Midwest. In summer, relative humidity maxima occur between 2 and 3 km during the overnight hours of 0300–0900 local solar time (LST). These maxima coincide with negative anomalies in temperature and positive anomalies in specific humidity. Vertical winds from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis dataset show that the height and diurnal timing of these positive relative humidity anomalies are consistent with the regional diurnal pattern of vertical motion. During the day, there is rising motion over the Rocky Mountains and subsidence over the Midwest, while conversely at night, there is sinking motion over the mountains and rising motion over the Midwest. The nocturnal relative humidity maxima over the Midwest are the strongest direct observational evidence to date of this mountain–plains solenoidal circulation, and provide a useful diagnostic for testing the strength of this circulation in climate and reanalysis models. There is significant interannual variability in the strength of the nocturnal relative humidity maxima. In 2011, the relative humidity maxima are very pronounced. In 2014, however, they are almost nonexistent. Finally, the relative humidity maxima are discussed in relation to the low-level jet (LLJ). The LLJ appears to be too low to directly contribute to the nocturnal relative humidity maxima.

scholarly journals Performance of the Vaisala RS80A/H and RS90 Humicap Sensors and the Meteolabor “Snow White” Chilled-Mirror Hygrometer in Paramaribo, Suriname

2006 ◽  
Vol 23 (11) ◽  
pp. 1506-1518 ◽  
Author(s):  
Gé Verver ◽  
Masatomo Fujiwara ◽  
Pier Dolmans ◽  
Cor Becker ◽  
Paul Fortuin ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Time Lag ◽  
Lower Troposphere ◽  
Upper Troposphere ◽  
Weather Forecasts ◽  
Snow White ◽  
Study Results ◽  
The Tropics ◽  
Humidity Profiles ◽  
Dry Bias

Abstract In climate research there is a strong need for accurate observations of water vapor in the upper atmosphere. Radiosoundings provide relative humidity profiles but the accuracy of many routine instruments is notoriously inadequate in the cold upper troposphere. In this study results from a soundings program executed in Paramaribo, Suriname (5.8°N, 55.2°W), are presented. The aim of this program was to compare the performance of different humidity sensors in the upper troposphere in the Tropics and to test different bias corrections suggested in the literature. The payload of each sounding consisted of a chilled-mirror “Snow White” sensor from Meteolabor AG, which was used as a reference, and two additional sensors from Vaisala, that is, either the RS80A, the RS80H, or the RS90. In total 37 separate soundings were made. For the RS80A a clear, dry bias of between −4% and −8% RH is found in the lower troposphere compared to the Snow White observation, confirming the findings in previous studies. A mean dry bias was found in the upper troposphere, which could be effectively corrected. The RS80H sensor shows a significant wet bias of 2%–5% in RH in the middle and upper troposphere, which has not been reported before. Comparing observations with RS80H sensors of different ages gives no indication of sensor aging or sensor contamination. It is therefore concluded that the plastic cover introduced by Vaisala to avoid sensor contamination is effective. Finally, the RS90 sensor yields a small but significant wet bias of 2%–3% below 7-km altitude. The time-lag error correction from Miloshevich et al. was applied to the Vaisala data, which resulted in an increased variability in the relative humidity profile above 9- (RS80A), 8- (RS80H), and 11-km (RS90) altitude, respectively, which is in better agreement with the Snow White data. The averaged Snow White profile is compared with the average profiles of relative humidity from the European Centre for Medium-Range Weather Forecasts (ECMWF). No significant bias is found in either the analyses or the forecasts. The correlation coefficient for the Snow White and ECMWF data between 200 and 800 hPa was 0.66 for the 36-h forecast and 0.77 for the analysis.

Multimodel Analysis of the Water Vapor Feedback in the Tropical Upper Troposphere

2006 ◽  
Vol 19 (20) ◽  
pp. 5455-5464 ◽  
Author(s):  
Ken Minschwaner ◽  
Andrew E. Dessler ◽  
Parnchai Sawaengphokhai
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Sea Surface ◽  
Specific Humidity ◽  
Upper Troposphere ◽  
The Mean ◽  
Tropical Upper Troposphere

Abstract Relationships between the mean humidity in the tropical upper troposphere and tropical sea surface temperatures in 17 coupled ocean–atmosphere global climate models were investigated. This analysis builds on a prior study of humidity and surface temperature measurements that suggested an overall positive climate feedback by water vapor in the tropical upper troposphere whereby the mean specific humidity increases with warmer sea surface temperature (SST). The model results for present-day simulations show a large range in mean humidity, mean air temperature, and mean SST, but they consistently show increases in upper-tropospheric specific humidity with warmer SST. The model average increase in water vapor at 250 mb with convective mean SST is 44 ppmv K−1, with a standard deviation of 14 ppmv K−1. Furthermore, the implied feedback in the models is not as strong as would be the case if relative humidity remained constant in the upper troposphere. The model mean decrease in relative humidity is −2.3% ± 1.0% K−1 at 250 mb, whereas observations indicate decreases of −4.8% ± 1.7% K−1 near 215 mb. These two values agree within the respective ranges of uncertainty, indicating that current global climate models are simulating the observed behavior of water vapor in the tropical upper troposphere with reasonable accuracy.

scholarly journals Tropospheric profiles of wet refractivity and humidity from the combination of remote sensing data sets and measurements on the ground

2013 ◽  
Vol 6 (11) ◽  
pp. 3083-3098 ◽  
Author(s):  
F. Hurter ◽  
O. Maier
Keyword(s):  
Relative Humidity ◽  
Lower Troposphere ◽  
Dew Point ◽  
Data Sets ◽  
Temperature Profiles ◽  
Least Squares Collocation ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Reconstruction Quality ◽  
Humidity Profiles

Abstract. We reconstruct atmospheric wet refractivity profiles for the western part of Switzerland with a least-squares collocation approach from data sets of (a) zenith path delays that are a byproduct of the GPS (global positioning system) processing, (b) ground meteorological measurements, (c) wet refractivity profiles from radio occultations whose tangent points lie within the study area, and (d) radiosonde measurements. Wet refractivity is a parameter partly describing the propagation of electromagnetic waves and depends on the atmospheric parameters temperature and water vapour pressure. In addition, we have measurements of a lower V-band microwave radiometer at Payerne. It delivers temperature profiles at high temporal resolution, especially in the range from ground to 3000 m a.g.l., though vertical information content decreases with height. The temperature profiles together with the collocated wet refractivity profiles provide near-continuous dew point temperature or relative humidity profiles at Payerne for the study period from 2009 to 2011. In the validation of the humidity profiles, we adopt a two-step procedure. We first investigate the reconstruction quality of the wet refractivity profiles at the location of Payerne by comparing them to wet refractivity profiles computed from radiosonde profiles available for that location. We also assess the individual contributions of the data sets to the reconstruction quality and demonstrate a clear benefit from the data combination. Secondly, the accuracy of the conversion from wet refractivity to dew point temperature and relative humidity profiles with the radiometer temperature profiles is examined, comparing them also to radiosonde profiles. For the least-squares collocation solution combining GPS and ground meteorological measurements, we achieve the following error figures with respect to the radiosonde reference: maximum median offset of relative refractivity error is −16% and quartiles are 5% to 40% for the lower troposphere. We further added 189 radio occultations that met our requirements. They mostly improved the accuracy in the upper troposphere. Maximum median offsets have decreased from 120% relative error to 44% at 8 km height. Dew point temperature profiles after the conversion with radiometer temperatures compare to radiosonde profiles as to: absolute dew point temperature errors in the lower troposphere have a maximum median offset of −2 K and maximum quartiles of 4.5 K. For relative humidity, we get a maximum mean offset of 7.3%, with standard deviations of 12–20%. The methodology presented allows us to reconstruct humidity profiles at any location where temperature profiles, but no atmospheric humidity measurements other than from GPS are available. Additional data sets of wet refractivity are shown to be easily integrated into the framework and strongly aid the reconstruction. Since the used data sets are all operational and available in near-realtime, we envisage the methodology of this paper to be a tool for nowcasting of clouds and rain and to understand processes in the boundary layer and at its top.

scholarly journals Anomalous holiday precipitation over southern China

2018 ◽  
Author(s):  
Jiahui Zhang ◽  
Dao-Yi Gong ◽  
Rui Mao ◽  
Jing Yang ◽  
Ziyin Zhang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Cloud Cover ◽  
Southern China ◽  
Lower Troposphere ◽  
Specific Humidity ◽  
Precipitation Frequency ◽  
Middle Troposphere ◽  
Low Cloud ◽  
The Mean

Abstract. The Chinese Spring Festival (CSF) is the most important festival in China. Officially, this holiday lasts approximately one week. Based on the long-term station observations from 1979 to 2012, this manuscript reports that during the holidays, the precipitation over southern China (108° E–123° E and 21° N–33° N, 155 stations) has been significantly reduced. The precipitation frequency anomalies from the fourth day to the sixth day after Lunar New Year's Day (i.e., days [+4, +6]) were found to decrease by −7.4 %. At the same time, the daily precipitation amounts experienced a reduction of −0.62 mm d−1 during days +2 to +5. The holiday precipitation anomalies are strongly linked to the relative humidity (ΔRH) and cloud cover. The station observations of the ΔRH showed an evident decrease from day +2 to +7, and a minimum appeared on days [+4, +6], with a mean of −3.9 %. The ΔRH vertical profile displays a significant drying below approximately 800 hPa. Between 800 hPa and 1000 hPa, the mean ΔRH is −3.9 %. The observed station daytime low cloud cover (LCC) evidently decreased by −6.1 % during days [+4, +6]. Meanwhile, the ERA-Interim daily LCC also shows a comparable reduction of −5.0 %. The anomalous relative humidity is mainly caused by the lower water vapor in the lower-middle troposphere. Evident negative specific humidity anomalies persist from day −3 to day +7 in the station observations. The average specific humidity anomaly for days [+4, +6] is −0.73 g kg−1. When the precipitation days exclude the mean, the anomaly remains significant, being −0.46 g kg−1. A significant deficit of water vapor is observed in the lower troposphere below 700 hPa. Between 800 hPa and 1000 hPa, the mean specific humidity dropped by −0.70 g kg−1. This drier lower-middle troposphere is due to anomalous northerly winds. Authors have proposed that the anomalous atmospheric circulation is likely related to the holiday aerosol anomaly. Station and satellite observations show that the East Asian aerosol concentrations during the CSF decrease evidently, the largest reduction occurring on days [−3, −1]. At the same time, a concurrent cooling is observed in the lower troposphere. In addition, an anomalous low pressure tilting westward occurs in the troposphere over East Asia. The anomalous cold advection seems to help trigger/strengthen a cyclonic circulation anomaly, which is responsible for the northerly winds and the less precipitation around the holidays. This possible mechanism needs further clarification by elaborate observation analysis and modeling.

scholarly journals Optimal estimation of tropospheric H<sub>2</sub>O and δD with IASI/METOP

2011 ◽  
Vol 11 (21) ◽  
pp. 11207-11220 ◽  
Author(s):  
M. Schneider ◽  
F. Hase
Keyword(s):  
A Priori ◽  
Measurement Techniques ◽  
Lower Troposphere ◽  
Optimal Estimation ◽  
Atmospheric Temperature ◽  
Data Sets ◽  
Upper Troposphere ◽  
Systematic Uncertainties ◽  
Better Than

Abstract. We present optimal estimates of tropospheric H2O and δD derived from radiances measured by the instrument IASI (Infrared Atmospheric Sounding Interferometer) flown on EUMETSAT's polar orbiter METOP. We document that the IASI spectra allow for retrieving H2O profiles between the surface and the upper troposphere as well as middle tropospheric δD values. A theoretical error estimation suggests a precision for H2O of better than 35% in the lower troposphere and of better than 15% in the middle and upper troposphere, respectively, whereby surface emissivity and atmospheric temperature uncertainties are the leading error sources. For the middle tropospheric δD values we estimate a precision of 15–20‰ with the measurement noise being the dominating error source. The accuracy of the IASI products is estimated to about 20–10% and 10‰ for lower to upper tropospheric H2O and middle tropospheric δD, respectively. It is limited by systematic uncertainties in the applied spectroscopic parameters and the a priori atmospheric temperature profiles. We compare our IASI products to a large number of near coincident radiosonde in-situ and ground-based FTS (Fourier Transform Spectrometer) remote sensing measurements. The bias and the scatter between the different H2O and δD data sets are consistent with the combined theoretical uncertainties of the involved measurement techniques.

scholarly journals Optimal estimation of tropospheric H<sub>2</sub>O and δD with IASI/METOP

2011 ◽  
Vol 11 (5) ◽  
pp. 16107-16146 ◽  
Author(s):  
M. Schneider ◽  
F. Hase
Keyword(s):  
Lower Troposphere ◽  
Optimal Estimation ◽  
Atmospheric Temperature ◽  
Data Sets ◽  
Error Sources ◽  
Upper Troposphere ◽  
Significant Bias ◽  
Atmospheric Sounding ◽  
Better Than

Abstract. We present an optimal estimation retrieval for tropospheric H2O and δD applying thermal nadir spectra measured by the instrument IASI (Infrared Atmospheric Sounding Interferometer) flown on EUMETSAT's polar orbiter METOP. We document that the IASI spectra allow for retrieving H2O profiles between the surface and the upper troposphere as well as middle tropospheric δD values. A theoretical error estimation suggests a precision for H2O of better than 35 % in the lower troposphere and of better than 15 % in the middle and upper troposphere, respectively, whereby surface emissivity and atmospheric temperature uncertainties are the leading error sources. For the middle tropospheric δD values we estimate a precision of 15–20‰, with the measurement noise being the dominating error source. We compare our IASI products to a large number of quasi coincident radiosonde in-situ and ground-based FTS (Fourier Transform Spectrometer) remote sensing measurements and find no significant bias between the H2O and δD data obtained by the different techniques. Furthermore, the scatter between the different data sets confirms our theoretical precision estimates.

scholarly journals Global carbon monoxide products from combined AIRS, TES and MLS measurements on A-train satellites

2014 ◽  
Vol 14 (1) ◽  
pp. 103-114 ◽  
Author(s):  
J. X. Warner ◽  
R. Yang ◽  
Z. Wei ◽  
F. Carminati ◽  
A. Tangborn ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Data Fusion ◽  
Satellite Data ◽  
Lower Troposphere ◽  
Data Sets ◽  
Data Set ◽  
Upper Troposphere ◽  
In Situ Data ◽  
Spatial Coverage

Abstract. This study tests a novel methodology to add value to satellite data sets. This methodology, data fusion, is similar to data assimilation, except that the background model-based field is replaced by a satellite data set, in this case AIRS (Atmospheric Infrared Sounder) carbon monoxide (CO) measurements. The observational information comes from CO measurements with lower spatial coverage than AIRS, namely, from TES (Tropospheric Emission Spectrometer) and MLS (Microwave Limb Sounder). We show that combining these data sets with data fusion uses the higher spectral resolution of TES to extend AIRS CO observational sensitivity to the lower troposphere, a region especially important for air quality studies. We also show that combined CO measurements from AIRS and MLS provide enhanced information in the UTLS (upper troposphere/lower stratosphere) region compared to each product individually. The combined AIRS–TES and AIRS–MLS CO products are validated against DACOM (differential absorption mid-IR diode laser spectrometer) in situ CO measurements from the INTEX-B (Intercontinental Chemical Transport Experiment: MILAGRO and Pacific phases) field campaign and in situ data from HIPPO (HIAPER Pole-to-Pole Observations) flights. The data fusion results show improved sensitivities in the lower and upper troposphere (20–30% and above 20%, respectively) as compared with AIRS-only version 5 CO retrievals, and improved daily coverage compared with TES and MLS CO data.

scholarly journals Influence of tropical cyclones on tropospheric ozone: possible implication

2015 ◽  
Vol 15 (13) ◽  
pp. 19305-19323 ◽  
Author(s):  
S. S. Das ◽  
M. V. Ratnam ◽  
K. N. Uma ◽  
K. V. Subrahmanyam ◽  
I. A. Girach ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Relative Humidity ◽  
Tropical Cyclones ◽  
Tropospheric Ozone ◽  
Potential Temperature ◽  
Lower Troposphere ◽  
Upper Troposphere ◽  
Dry Air ◽  
Cyclonic Storms

Abstract. The present study examines the role of tropical cyclones in the enhancement of tropospheric ozone. The most significant and new observation is the increase in the upper tropospheric (10–16 km) ozone by 20–50 ppbv, which has extended down to the middle (6–10 km) and lower troposphere (< 6 km). The descending rate of enhanced ozone layer is found to be 0.87–1 km day−1. Numerical simulation of potential vorticity, vertical velocity and potential temperature indicate the intrusion of ozone from the upper troposphere to the surface. Space borne observations of relative humidity indicate the presence of sporadic dry air in the upper and middle troposphere over the cyclonic region. These observations constitute quantitatively an experimental evidence of enhanced tropospheric ozone during cyclonic storms.

scholarly journals Global 3D Features of Error Variances of GPS Radio Occultation and Radiosonde Observations

2020 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Xu Xu ◽  
Xiaolei Zou
Keyword(s):  
Radio Occultation ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Error Variance ◽  
Specific Humidity ◽  
Temperature Error ◽  
Observation Error ◽  
Bending Angle ◽  
Gps Radio Occultation ◽  
Low Latitudes

Global Positioning System (GPS) radio occultation (RO) and radiosonde (RS) observations are two major types of observations assimilated in numerical weather prediction (NWP) systems. Observation error variances are required input that determines the weightings given to observations in data assimilation. This study estimates the error variances of global GPS RO refractivity and bending angle and RS temperature and humidity observations at 521 selected RS stations using the three-cornered hat method with additional ERA-Interim reanalysis and Global Forecast System forecast data available from 1 January 2016 to 31 August 2019. The global distributions, of both RO and RS observation error variances, are analyzed in terms of vertical and latitudinal variations. Error variances of RO refractivity and bending angle and RS specific humidity in the lower troposphere, such as at 850 hPa (3.5 km impact height for the bending angle), all increase with decreasing latitude. The error variances of RO refractivity and bending angle and RS specific humidity can reach about 30 N-unit2, 3 × 10−6 rad2, and 2 (g kg−1)2, respectively. There is also a good symmetry of the error variances of both RO refractivity and bending angle with respect to the equator between the Northern and Southern Hemispheres at all vertical levels. In this study, we provide the mean error variances of refractivity and bending angle in every 5°-latitude band between the equator and 60°N, as well as every interval of 10 hPa pressure or 0.2 km impact height. The RS temperature error variance distribution differs from those of refractivity, bending angle, and humidity, which, at low latitudes, are smaller (less than 1 K2) than those in the midlatitudes (more than 3 K2). In the midlatitudes, the RS temperature error variances in North America are larger than those in East Asia and Europe, which may arise from different radiosonde types among the above three regions.

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