scholarly journals Signatures of the 2-day wave and sudden stratospheric warmings in Arctic water vapour observed by ground-based microwave radiometry

2015 ◽  
Vol 15 (9) ◽  
pp. 5099-5108 ◽  
Author(s):  
B. Tschanz ◽  
N. Kämpfer
Keyword(s):  
Time Series ◽  
Water Vapour ◽  
Temporal Resolution ◽  
Microwave Radiometer ◽  
Maximum Amplitude ◽  
Microwave Radiometry ◽  
The Arctic ◽  
Arctic Water ◽  
Measurement Period

Abstract. The ground-based microwave radiometer MIAWARA-C recorded the upper stratospheric and lower mesospheric water vapour distribution continuously from June 2011 to March 2013 above the Arctic station of Sodankylä, Finland (67.4° N, 26.6° E) without major interruptions and offers water vapour profiles with temporal resolution of 1 h for average conditions. The water vapour time series of MIAWARA-C shows strong periodic variations in both summer and winter related to the quasi-2-day wave. Above 0.1 hPa the amplitudes are strongest in summer. The stratospheric wintertime 2-day wave is pronounced for both winters on altitudes below 0.1 hPa and reaches a maximum amplitude of 0.8 ppmv in November 2011. Over the measurement period, the instrument monitored the changes in water vapour linked to two sudden stratospheric warmings in early 2012 and 2013. Based on the water vapour measurements, the descent rate in the vortex after the warmings is 364 m d−1 for 2012 and 315 m d−1 for 2013.

scholarly journals Signatures of the two day wave and sudden stratospheric warmings in Arctic water vapour observed by ground-based microwave radiometry

2015 ◽  
Vol 15 (1) ◽  
pp. 371-392
Author(s):  
B. Tschanz ◽  
N. Kämpfer
Keyword(s):  
Time Series ◽  
Water Vapour ◽  
Temporal Resolution ◽  
Microwave Radiometer ◽  
Maximum Amplitude ◽  
Microwave Radiometry ◽  
The Arctic ◽  
Arctic Water ◽  
Measurement Period

Abstract. The ground-based microwave radiometer MIAWARA-C recorded the upper stratospheric and lower mesospheric water vapour distribution continuously from June 2011 to March 2013 above the Arctic station of Sodankylä, Finland (67.4° N, 26.6° E) without major interruptions and offers water vapour profiles with temporal resolution of one hour for average conditions. Over the measurement period, the instrument monitored the changes in water vapour linked to two sudden stratospheric warmings in early 2012 and 2013. Based on the water vapour measurements, the descent rate in the vortex after the warmings is 364 m d−1 for 2012 and 315 m d−1 for 2013. The water vapour time series of MIAWARA-C shows strong periodic variations in both summer and winter related to the quasi two day wave. In the mesosphere the amplitudes are strongest in summer. The stratospheric wintertime two day wave is pronounced for both winters and reaches a maximum amplitude of 0.8 ppmv in November 2011.

scholarly journals Pan-Arctic measurements of wintertime water vapour column using a satellite-borne microwave radiometer

2019 ◽  
Author(s):  
Christopher Perro ◽  
Thomas J. Duck ◽  
Glen Lesins ◽  
Kimberly Strong ◽  
Penny M. Rowe ◽  
...  
Keyword(s):  
Water Vapour ◽  
Western Europe ◽  
Microwave Radiometer ◽  
Advanced Technology ◽  
The Arctic ◽  
Satellite Measurements ◽  
Arctic Water ◽  
Surface Reflection ◽  
Humidity Measurements ◽  
Additional Point

Abstract. A methodology for retrieving high-latitude winter water vapour columns from passive microwave satellite measurements from Perro et al. (2016) is extended to use measured surface reflectance ratios under more realistic surface reflection assumptions. Pan-Arctic wintertime water vapour is retrieved from Advanced Technology Microwave Sounder (ATMS) measurements made from January 2012 through March 2015 (December to March). The water vapour retrievals are validated using two ground based instruments: the G-band Vapor Radiometer (GVR) at Barrow, Alaska, and the Extended-Range Atmospheric Emitted Radiance Interferometer (E-AERI) at Eureka, Nunavut. E-AERI was chosen as an additional point of validation compared to Perro et al. (2016) due to the different technology and frequencies employed to determine water vapour column compared to the ATMS and GVR. For water vapour columns less than 6 kg m−2, the biases are +2.6 % and +0.01 % relative to the GVR and E-AERI, respectively. A comparison with radiosonde humidity measurements shows they are dry relative to the ATMS measurements in North America and Western Europe, and moist in Asia and Eastern Europe, with an apparent dependence on radiosonde manufacturer. Reanalyses (ERA-5, ERA-Interim, ASR V2, JRA-55 and NCEP) are systematically drier than the ATMS measurements for water vapour columns less than 6 kg m−2, with relative biases ranging from −10 % to −23 %. These differences could have implications for the understanding of the Arctic water budget and climate.

scholarly journals Investigation of Arctic middle-atmospheric dynamics using 3 years of H<sub>2</sub>O and O<sub>3</sub> measurements from microwave radiometers at Ny-Ålesund

2019 ◽  
Author(s):  
Franziska Schranz ◽  
Brigitte Tschanz ◽  
Rolf Rüfenacht ◽  
Klemens Hocke ◽  
Mathias Palm ◽  
...  
Keyword(s):  
Water Vapour ◽  
Middle Atmosphere ◽  
Microwave Radiometer ◽  
Polar Vortex ◽  
The Arctic ◽  
High Time Resolution ◽  
Middle Atmospheric Dynamics ◽  
Wind Profiles ◽  
Microwave Radiometers

Abstract. We use 3 years of water vapour and ozone measurements to analyse dynamical events in the polar middle atmosphere such as sudden stratospheric warmings (SSW), polar vortex shifts, water vapour descent rates and periodicities. The measurements were performed with the two ground-based microwave radiometers MIAWARA-C and GROMOS-C which are co-located at the AWIPEV research base at Ny-Ålesund, Svalbard (79° N, 12° E) since September 2015. The almost continuous datasets of water vapour and ozone are characterised by a high time resolution in the order of hours. A thorough intercomparison of these datasets with models and measurements from satellite, ground-based and in-situ instruments was performed. In the upper stratosphere and lower mesosphere the MIAWARA-C profiles agree within 5 % with SD-WACCM simulations and ACE-FTS measurements whereas AuraMLS measurements show an average offset of 10–15 % depending on altitude but constant in time. Stratospheric GROMOS-C profiles are within 5 % of the satellite instruments AuraMLS and ACE-FTS and the ground-based microwave radiometer OZORAM which is also located at Ny-Ålesund. During these first three years of the measurement campaign typical phenomena of the Arctic middle atmosphere took place and we analysed their signatures in the water vapour and ozone datasets. Inside of the polar vortex in autumn we found the descent rate of mesospheric water vapour to be 435 m/day on average. In early 2017 distinct increases in mesospheric water vapour of about 2 ppm were observed when the polar vortex was displaced and midlatitude air was brought to Ny-Ålesund. Two major sudden stratospheric warmings took place in March 2016 and February 2018 where ozone enhancements of up to 4 ppm were observed. The zonal wind reversals accompanying a major SSW were captured in the GROMOS-C wind profiles which are retrieved from the ozone spectra. After the SSW in February 2018 the polar vortex re-established and the water vapour descent rate in the mesosphere was 355 m/day. In the water vapour and ozone time series signatures of atmospheric waves with periods close to 2, 5, 10 and 16 days were found.

scholarly journals Tropospheric water vapour above Switzerland over the last 12 years

2009 ◽  
Vol 9 (16) ◽  
pp. 5975-5988 ◽  
Author(s):  
J. Morland ◽  
M. Collaud Coen ◽  
K. Hocke ◽  
P. Jeannet ◽  
C. Mätzler
Keyword(s):  
Time Series ◽  
Water Vapour ◽  
Grid Point ◽  
Microwave Radiometer ◽  
Regional Scale ◽  
Seasonal Trends ◽  
Climate Change Effects ◽  
Integrated Water Vapour ◽  
Mountain Weather

Abstract. Integrated Water vapour (IWV) has been measured since 1994 by the TROWARA microwave radiometer in Bern, Switzerland. Homogenization techniques were used to identify and correct step changes in IWV related to instrument problems. IWV from radiosonde, GPS and sun photometer (SPM) was used in the homogenisation process as well as partial IWV columns between valley and mountain weather stations. The average IWV of the homogenised TROWARA time series was 14.4 mm over the 1996–2007 period, with maximum and minimum monthly average values of 22.4 mm and 8 mm occurring in August and January, respectively. A weak diurnal cycle in TROWARA IWV was detected with an amplitude of 0.32 mm, a maximum at 21:00 UT and a minimum at 11:00 UT. For 1996–2007, TROWARA trends were compared with those calculated from the Payerne radiosonde and the closest ECMWF grid point to Bern. Using least squares analysis, the IWV time series of radiosondes at Payerne, ECMWF, and TROWARA showed consistent positive trends from 1996 to 2007. The radiosondes measured an IWV trend of 0.45±0.29%/y, the TROWARA radiometer observed a trend of 0.39±0.44%/y, and ECMWF operational analysis gave a trend of 0.25±0.34%/y. Since IWV has a strong and variable annual cycle, a seasonal trend analysis (Mann-Kendall analysis) was also performed. The seasonal trends are stronger by a factor 10 or so compared to the full year trends above. The positive IWV trends of the summer months are partly compensated by the negative trends of the winter months. The strong seasonal trends of IWV on regional scale underline the necessity of long-term monitoring of IWV for detection,understanding, and forecast of climate change effects in the Alpine region.

scholarly journals Comparison of total water vapour content in the Arctic derived from GNSS, AIRS, MODIS and SCIAMACHY

2018 ◽  
Vol 11 (5) ◽  
pp. 2949-2965 ◽  
Author(s):  
Dunya Alraddawi ◽  
Alain Sarkissian ◽  
Philippe Keckhut ◽  
Olivier Bock ◽  
Stefan Noël ◽  
...  
Keyword(s):  
Time Series ◽  
Water Vapour ◽  
Satellite Data ◽  
Cloud Cover ◽  
Surface Albedo ◽  
Hydrological Cycle ◽  
Radiation Budget ◽  
The Arctic ◽  
Gnss Measurements ◽  
Gnss Time Series

Abstract. Atmospheric water vapour plays a key role in the Arctic radiation budget, hydrological cycle and hence climate, but its measurement with high accuracy remains an important challenge. Total column water vapour (TCWV) datasets derived from ground-based GNSS measurements are used to assess the quality of different existing satellite TCWV datasets, namely from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Atmospheric Infrared Sounder (AIRS) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The comparisons between GNSS and satellite data are carried out for three reference Arctic observation sites (Sodankylä, Ny-Ålesund and Thule) where long homogeneous GNSS time series of more than a decade (2001–2014) are available. We select hourly GNSS data that are coincident with overpasses of the different satellites over the three sites and then average them into monthly means that are compared with monthly mean satellite products for different seasons. The agreement between GNSS and satellite time series is generally within 5 % at all sites for most conditions. The weakest correlations are found during summer. Among all the satellite data, AIRS shows the best agreement with GNSS time series, though AIRS TCWV is often slightly too high in drier atmospheres (i.e. high-latitude stations during autumn and winter). SCIAMACHY TCWV data are generally drier than GNSS measurements at all the stations during the summer. This study suggests that these biases are associated with cloud cover, especially at Ny-Ålesund and Thule. The dry biases of MODIS and SCIAMACHY observations are most pronounced at Sodankylä during the snow season (from October to March). Regarding SCIAMACHY, this bias is possibly linked to the fact that the SCIAMACHY TCWV retrieval does not take accurately into account the variations in surface albedo, notably in the presence of snow with a nearby canopy as in Sodankylä. The MODIS bias at Sodankylä is found to be correlated with cloud cover fraction and is also expected to be affected by other atmospheric or surface albedo changes linked for instance to the presence of forests or anthropogenic emissions. Overall, the results point out that a better estimation of seasonally dependent surface albedo and a better consideration of vertically resolved cloud cover are recommended if biases in satellite measurements are to be reduced in the polar regions.

scholarly journals Validation of middle atmospheric campaign-based water vapour measured by the ground-based microwave radiometer MIAWARA-C

2013 ◽  
Vol 6 (1) ◽  
pp. 1311-1359 ◽  
Author(s):  
B. Tschanz ◽  
C. Straub ◽  
D. Scheiben ◽  
K. A. Walker ◽  
G. P. Stiller ◽  
...  
Keyword(s):  
Water Vapour ◽  
Random Error ◽  
Microwave Radiometer ◽  
Incident Radiation ◽  
Temporal Variations ◽  
The Arctic ◽  
Atmospheric Water ◽  
Data Set ◽  
Atmospheric Water Vapour ◽  
Measurement Campaigns

Abstract. Middle atmospheric water vapour can be used as a tracer for dynamical processes. It is mainly measured by satellite instruments and ground-based microwave radiometers. Ground-based instruments capable of measuring middle atmospheric water vapour are sparse but valuable as they complement satellite measurements, are relatively easy to maintain and have a long lifetime. MIAWARA-C is a ground-based microwave radiometer for middle atmospheric water vapour designed for use on measurement campaigns for both atmospheric case studies and instrument intercomparisons. MIAWARA-C's retrieval version 1.1 (v1.1) is set up in a way to provide a consistent data set even if the instrument is operated from different locations on a campaign basis. The sensitive altitude range for v1.1 extends from 4 hPa (37 km) to 0.017 hPa (75 km). MIAWARA-C measures two polarisations of the incident radiation in separate receiver channels and can therefore provide two independent measurements of the same air mass. The standard deviation of the difference between the profiles obtained from the two polarisations is in excellent agreement with the estimated random error of v1.1. In this paper, the quality of v1.1 data is assessed during two measurement campaigns: (1) five months of measurements in the Arctic (Sodankylä, 67.37° N/26.63° E) and (2) nine months of measurements at mid-latitudes (Zimmerwald, 46.88° N/7.46° E). For both campaigns MIAWARA-C's profiles are compared to measurements from the satellite experiments Aura MLS and MIPAS. In addition, comparisons to ACE-FTS and SOFIE are presented for the Arctic and to the ground-based radiometer MIAWARA for the mid-latitudinal campaign. In general all intercomparisons show high correlation coefficients, above 0.5 at altitudes above 45 km, confirming the ability of MIAWARA-C to monitor temporal variations on the order of days. The biases are generally below 10% and within the estimated systematic uncertainty of MIAWARA-C. No consistent wet or dry bias is identified for MIAWARA-C. In addition, comparisons to the reference instruments indicate the estimated random error of v1.1 to be a realistic measure of the random variation on the retrieved profile.

scholarly journals Ground-based remote sensing of thin clouds in the Arctic

2013 ◽  
Vol 6 (5) ◽  
pp. 1227-1243 ◽  
Author(s):  
T. J. Garrett ◽  
C. Zhao
Keyword(s):  
Water Vapour ◽  
Thermal Emission ◽  
Stratospheric Ozone ◽  
Microwave Radiometer ◽  
Single Layer ◽  
Primary Source ◽  
The Arctic ◽  
Liquid Water Path ◽  
Water Path ◽  
Microphysical Properties

Abstract. This paper describes a method for using interferometer measurements of downwelling thermal radiation to retrieve the properties of single-layer clouds. Cloud phase is determined from ratios of thermal emission in three "micro-windows" at 862.5 cm−1, 935.8 cm−1, and 988.4 cm−1 where absorption by water vapour is particularly small. Cloud microphysical and optical properties are retrieved from thermal emission in the first two of these micro-windows, constrained by the transmission through clouds of primarily stratospheric ozone emission at 1040 cm−1. Assuming a cloud does not approximate a blackbody, the estimated 95% confidence retrieval errors in effective radius re, visible optical depth τ, number concentration N, and water path WP are, respectively, 10%, 20%, 38% (55% for ice crystals), and 16%. Applied to data from the Atmospheric Radiation Measurement programme (ARM) North Slope of Alaska – Adjacent Arctic Ocean (NSA-AAO) site near Barrow, Alaska, retrievals show general agreement with both ground-based microwave radiometer measurements of liquid water path and a method that uses combined shortwave and microwave measurements to retrieve re, τ and N. Compared to other retrieval methods, advantages of this technique include its ability to characterise thin clouds year round, that water vapour is not a primary source of retrieval error, and that the retrievals of microphysical properties are only weakly sensitive to retrieved cloud phase. The primary limitation is the inapplicability to thicker clouds that radiate as blackbodies and that it relies on a fairly comprehensive suite of ground based measurements.

scholarly journals Estimating Snow Depth on Arctic Sea Ice using Satellite Microwave Radiometry and a Neural Network

2019 ◽  
Author(s):  
Anne Braakmann-Folgmann ◽  
Craig Donlon
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sea Ice ◽  
Snow Depth ◽  
Microwave Radiometer ◽  
Microwave Radiometry ◽  
The Arctic ◽  
Network Approach ◽  
Neural Network Approach ◽  
Satellite Microwave

Abstract. Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo, the Arctic freshwater budget, and influences the Arctic climate: it is fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge. A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath > 1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high inclination dawn-dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are obtained even outside the algorithm training period and area. We then convert CryoSat freeboard measurements to SIT using different snow products including the snow depth from our networks. We confirm that a more accurate snow depth product derived using our neural networks leads to more accurate estimates of SIT, when compared to the SIT measured by a laser altimeter at the OIB campaign. Our network with additional SMOS input yields even higher accuracies, but has the disadvantage of a larger “hole at the pole”. Our neural network approaches are applicable over the whole Arctic, capturing first-year ice and multi-year ice conditions throughout winter. Once the networks are designed and trained, they are fast and easy to use. The combined AMSR2 + SMOS neural network is particularly important as a pre-cursor demonstration for the Copernicus CIMR candidate mission highlighting the benefit of CIMR.

scholarly journals Time series inversion of spectra from ground-based radiometers

2013 ◽  
Vol 6 (7) ◽  
pp. 1597-1609 ◽  
Author(s):  
O. M. Christensen ◽  
P. Eriksson
Keyword(s):  
Time Series ◽  
Water Vapour ◽  
A Priori ◽  
Microwave Radiometer ◽  
Retrieval Algorithm ◽  
Temporal Correlations ◽  
Temporal Smoothing ◽  
Temporal Averaging ◽  
Diagnostic Values ◽  
A Priori Uncertainty

Abstract. Retrieving time series of atmospheric constituents from ground-based spectrometers often requires different temporal averaging depending on the altitude region in focus. This can lead to several datasets existing for one instrument, which complicates validation and comparisons between instruments. This paper puts forth a possible solution by incorporating the temporal domain into the maximum a posteriori (MAP) retrieval algorithm. The state vector is increased to include measurements spanning a time period, and the temporal correlations between the true atmospheric states are explicitly specified in the a priori uncertainty matrix. This allows the MAP method to effectively select the best temporal smoothing for each altitude, removing the need for several datasets to cover different altitudes. The method is compared to traditional averaging of spectra using a simulated retrieval of water vapour in the mesosphere. The simulations show that the method offers a significant advantage compared to the traditional method, extending the sensitivity an additional 10 km upwards without reducing the temporal resolution at lower altitudes. The method is also tested on the Onsala Space Observatory (OSO) water vapour microwave radiometer confirming the advantages found in the simulation. Additionally, it is shown how the method can interpolate data in time and provide diagnostic values to evaluate the interpolated data.

scholarly journals Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network

2019 ◽  
Vol 13 (9) ◽  
pp. 2421-2438 ◽  
Author(s):  
Anne Braakmann-Folgmann ◽  
Craig Donlon
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sea Ice ◽  
Snow Depth ◽  
Microwave Radiometer ◽  
Microwave Radiometry ◽  
The Arctic ◽  
Network Approach ◽  
Neural Network Approach ◽  
Satellite Microwave

Abstract. Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo and the Arctic freshwater budget, and it influences the Arctic climate: it is a fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge. A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath >1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high-inclination dawn–dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are obtained even outside the algorithm training period and area. We then convert CryoSat freeboard measurements to SIT using different snow products including the snow depth from our networks. We confirm that a more accurate snow depth product derived using our neural networks leads to more accurate estimates of SIT, when compared to the SIT measured by a laser altimeter at the OIB campaign. Our network with additional SMOS input yields even higher accuracies, but has the disadvantage of a larger “hole at the pole”. Our neural network approaches are applicable over the whole Arctic, capturing first-year ice and multi-year ice conditions throughout winter. Once the networks are designed and trained, they are fast and easy to use. The combined AMSR2 + SMOS neural network is particularly important as a precursor demonstration for the Copernicus CIMR candidate mission highlighting the benefit of CIMR.

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