Technical Note: 30 years of HIRS data of upper tropospheric humidity

We use 30 years of intercalibrated HIRS (High-Resolution Infrared Radiation Sounder) data to produce a 30-year data set of upper tropospheric humidity with respect to ice (UTH_i). Since the required brightness temperatures (channels 12 and 6, <i>T</i>₁₂ and <i>T</i>₆) are intercalibrated to different versions of the HIRS sensors (HIRS/2 and HIRS/4) it is necessary to convert the channel 6 brightness temperatures which are intercalibrated to HIRS/4 into equivalent brightness temperatures intercalibrated to HIRS/2, which is achieved using a linear regression. Using the new regression coefficients we produce daily files of UTH_i, <i>T</i>₁₂ and <i>T</i>₆, for each NOAA satellite and METOP-A (Meteorological Operational Satellite Programme), which carry the HIRS instrument. From this we calculate daily and monthly means in 2.5° &times; 2.5° resolution for the northern midlatitude zone 30–60° N. As a first application we calculate decadal means of UTH_i and the brightness temperatures for the two decades 1980–1989 and 2000–2009. We find that the humidity mainly increased from the 1980s to the 2000s and that this increase is highly statistically significant in large regions of the considered midlatitude belt. The main reason for this result and its statistical significance is the corresponding increase of the <i>T</i>₁₂ variance. Changes of the mean brightness temperatures are less significant

On the interpretation of upper-tropospheric humidity based on a second-order retrieval from infrared radiances

We present a novel retrieval for upper-tropospheric humidity (UTH) from High-resolution Infrared Radiation Sounder (HIRS) channel 12 radiances that successfully bridges the wavelength change from 6.7 to 6.5&thinsp;µm that occurred from HIRS/2 on National Oceanic and Atmospheric Administration satellite NOAA-14 to HIRS/3 on satellite NOAA-15. The jump in average brightness temperature (in the water vapour channel; T12) that this change had caused (about −7&thinsp;K) could be fixed with a statistical inter-calibration method (Shi and Bates, 2011). Unfortunately, the retrieval of UTHi (upper-tropospheric humidity with respect to ice) based on the inter-calibrated data was not satisfying at the high tail of the distribution of UTHi. Attempts to construct a better inter-calibration in the low T12 range (equivalent to the high UTHi range) were either not successful (Gierens et al., 2018) or required additional statistically determined corrections to the measured brightness temperatures (Gierens and Eleftheratos, 2017). The new method presented here is based on the original one (Soden and Bretherton, 1993; Stephens et al., 1996; Jackson and Bates, 2001), but it extends linearisations in the formulation of water vapour saturation pressure and in the temperature dependence of the Planck function to second order. To achieve the second-order formulation we derive the retrieval from the beginning, and we find that the most influential ingredient is the use of different optical constants for the two involved channel wavelengths (6.7 and 6.5&thinsp;µm). The result of adapting the optical constant is an almost perfect match between UTH data measured by HIRS/2 on NOAA-14 and HIRS/3 on NOAA-15 on 1004 common days of operation. The method is applied to both UTH and UTHi. For each case retrieval coefficients are derived. We present a number of test applications, e.g. on computed brightness temperatures based on high-resolution radiosonde profiles, on the brightness temperatures measured by the satellites on the mentioned 1004 common days of operation. Further, we present time series of the occurrence frequency of high UTHi cases, and we show the overall probability distribution of UTHi. The two latter applications expose indications of moistening of the upper troposphere over the last 35 years. Finally, we discuss the significance of UTH. We state that UTH algorithms cannot be judged for their correctness or incorrectness, since there is no true UTH. Instead, UTH algorithms should fulfill a number of usefulness postulates, which we suggest and discuss.</p

Evidence of impact of aviation on cirrus cloud formation

International audienceThis work examines changes in cirrus cloud cover (CCC) in possible association with aviation activities at congested air corridors. The analysis is based on the latest version of the International Satellite Cloud Climatology Project D2 data set and covers the period 1984-1998. Over the studied areas, the effect of large-scale modes of natural climate variability such as ENSO, QBO and NAO as well as the possible influence of the tropopause variability, were first removed from the cloud data set in order to calculate long-term changes of observed cirrus cloudiness. The results show increasing trends in (CCC) between 1984 and 1998 over the high air traffic corridors of North America, North Atlantic and Europe. Of these upward trends, only in the summertime over the North Atlantic and only in the wintertime over North America are statistically significant (exceeding +2.0% per decade). Over adjacent locations with low air traffic, the calculated trends are statistically insignificant and in most cases negative both during winter and summer in the regions studied. These negative trends, over low air traffic regions, are consistent with the observed large scale negative trends seen in (CCC) over most of the northern middle latitudes and over the tropics. Moreover, further investigation of vertical velocities over high and low air traffic regions provide evidence that the trends of opposite signs in (CCC) over these regions, do not seem to be caused by different trends in dynamics. It is also shown that the longitudinal distribution of decadal changes in (CCC) along the latitude belt centered at the North Atlantic air corridor, parallels the spatial distribution of fuel consumption from highflying air traffic, providing an independent test of possible impact of aviation on contrail cirrus formation. The correlation between the fuel consumption and the longitudinal variability of (CCC) is significant (+0.7) over the middle latitudes but not over the tropics. This could be explained by the fact that over the tropics the variability of (CCC) is dominated by dynamics while at middle latitudes microphysics explain most of its variability. Results from this study are compared with other studies and for different periods of records and it appears that there exists general agreement as to the evidence of a possible aviation effect on high cloud positive trends over regions with congested air traffic.</p

The use of QBO, ENSO, and NAO perturbations in the evaluation of GOME-2 MetOp A total ozone measurements

In this work we present evidence that quasi-cyclical perturbations in total ozone (quasi-biennial oscillation – QBO, El Niño–Southern Oscillation – ENSO, and North Atlantic Oscillation – NAO) can be used as independent proxies in evaluating Global Ozone Monitoring Experiment (GOME) 2 aboard MetOp A (GOME-2A) satellite total ozone data, using ground-based (GB) measurements, other satellite data, and chemical transport model calculations. The analysis is performed in the frame of the validation strategy on longer time scales within the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Atmospheric Composition Monitoring (AC SAF) project, covering the period 2007–2016. Comparison of GOME-2A total ozone with ground observations shows mean differences of about -0.7±1.4&thinsp;% in the tropics (0–30∘), about +0.1±2.1&thinsp;% in the mid-latitudes (30–60∘), and about +2.5±3.2&thinsp;% and 0.0±4.3&thinsp;% over the northern and southern high latitudes (60–80∘), respectively. In general, we find that GOME-2A total ozone data depict the QBO–ENSO–NAO natural fluctuations in concurrence with the co-located solar backscatter ultraviolet radiometer (SBUV), GOME-type Total Ozone Essential Climate Variable (GTO-ECV; composed of total ozone observations from GOME, SCIAMACHY – SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY, GOME-2A, and OMI – ozone monitoring instrument, combined into one homogeneous time series), and ground-based observations. Total ozone from GOME-2A is well correlated with the QBO (highest correlation in the tropics of +0.8) in agreement with SBUV, GTO-ECV, and GB data which also give the highest correlation in the tropics. The differences between deseazonalized GOME-2A and GB total ozone in the tropics are within ±1&thinsp;%. These differences were tested further as to their correlations with the QBO. The differences had practically no QBO signal, providing an independent test of the stability of the long-term variability of the satellite data. Correlations between GOME-2A total ozone and the Southern Oscillation Index (SOI) were studied over the tropical Pacific Ocean after removing seasonal, QBO, and solar-cycle-related variability. Correlations between ozone and the SOI are on the order of +0.5, consistent with SBUV and GB observations. Differences between GOME-2A and GB measurements at the station of Samoa (American Samoa; 14.25∘&thinsp;S, 170.6∘&thinsp;W) are within ±1.9&thinsp;%. We also studied the impact of the NAO on total ozone in the northern mid-latitudes in winter. We find very good agreement between GOME-2A and GB observations over Canada and Europe as to their NAO-related variability, with mean differences reaching the ±1&thinsp;% levels. The agreement and small differences which were found between the independently produced total ozone datasets as to the influence of the QBO, ENSO, and NAO show the importance of these climatological proxies as additional tool for monitoring the long-term stability of satellite–ground-truth biases.</p

Chapter 4: The LOTUS regression model

One of the primary motivations of the LOTUS effort is to attempt to reconcile the discrepancies in ozone trend results from the wealth of literature on the subject. Doing so requires investigating the various methodologies employed to derive long-term trends in ozone as well as to examine the large array of possible variables that feed into those methodologies and analyse their impacts on potential trend results. Given the limited amount of time, the LOTUS group focused on the most common methodology of multiple linear regression and performed a number of sensitivity tests with the goal of trying to establish best practices and come to a consensus on a single regression model to use for this study. This chapter discusses the details and results of the sensitivity tests before describing the components of the final single model that was chosen and the reasons for that choice

Technical note: On the intercalibration of HIRS channel 12 brightness temperatures following the transition from HIRS 2 to HIRS 3/4 for ice saturation studies

In the present study we explore the capability of the intercalibrated HIRS brightness temperature data at channel 12 (the HIRS water vapour channel; T 12) to reproduce ice supersaturation in the upper troposphere during the period 1979-2014. Focus is given on the transition from the HIRS 2 to the HIRS 3 instrument in the year 1999, which involved a shift of the central wavelength in channel 12 from 6.7 to 6.5 μm. It is shown that this shift produced a discontinuity in the time series of low T12 values ( &amp;amp;lt; 235 K) and associated cases of high upper-tropospheric humidity with respect to ice (UTHi&amp;gt; 70%) in the year 1999 which prevented us from maintaining a continuous, long-term time series of ice saturation throughout the whole record (1979-2014). We show that additional corrections are required to the low T12 values in order to bring HIRS 3 levels down to HIRS 2 levels. The new corrections are based on the cumulative distribution functions of T12 from NOAA 14 and 15 satellites (that is, when the transition from HIRS 2 to HIRS 3 occurred). By applying these corrections to the low T12 values we show that the discontinuity in the time series caused by the transition of HIRS 2 to HIRS 3 is not apparent anymore when it comes to calculating extreme UTHi cases. We come up with a new time series for values found at the low tail of the T12 distribution, which can be further exploited for analyses of ice saturation and supersaturation cases. The validity of the new method with respect to typical intercalibration methods such as regression-based methods is presented and discussed. © 2016 Author(s)

The contribution and weighting functions of radiative transfer – theory and application to the retrieval of upper-tropospheric humidity

Several interesting problems in remote sensing can be traced back to the question of the origin along the line of sight of the registered photons. In this paper we revive old concepts that directly follow from the equation of radiative transfer, namely the contribution and weighting functions. We give them, however, a new mathematical form by transforming them into a pair of probability density functions which have the advantage that they can be used in a more f exible manner. We derive these functions, demonstrate a simple relation between them and show how they can be used in principle. Then we proceed with simple applications to a case of upper-tropospheric humidity (UTH) retrieval. In particular, we show how the mean emission pressure level and mean emission temperature change with increasing UTH. We show that the mean emission pressure increases with increasing humidity and remains almost unchanged for UTH values greater than 50 %. The mean emission temperature is decreasing exponentially as UTH increases. The sensitivities of the mean emission pressure to various quantities, e.g. the temperature lapse rate, or retrieval situations, e.g. whether UTH or UTH with respect to ice is considered or which of two different versions of a receiver is used, is generally small compared to the 2σp-width of the layer. The relation of the contribution and weighting functions to Jacobians is discussed as well. We note that the dependence of the mean emission pressure level and other statistical quantities can be formulated using the radiances or brightness temperatures directly. The new method thus offers additional possibilities for interpretation of data from passive remote sensing, and examples are given. In addition of deriving the desired product (for instance, UTH) one can derive and map the mean emission location, its width, and other physical properties like mean temperature of the emission layer. The necessary probability density functions are contained in the solution of the radiative transfer equation and can thus be obtained from runs of the corresponding models. We recommend that radiative transfer models be equipped with facilities to compute and output the contribution and weighting functions. © 2020 The authors Gebrüder Borntraeger Science Publishers, Stuttgart, www.borntraeger-cramer.com

Intercalibration between HIRS/2 and HIRS/3 channel 12 based on physical considerations

High-resolution Infrared Radiation Sounder (HIRS) brightness temperatures at channel 12 (&lt;i&gt;T&lt;/i&gt;12) can be used to assess the water vapour content of the upper troposphere. The transition from HIRS/2 to HIRS/3 in 1999 involved a shift in the central wavelength of channel 12 from 6.7 to 6.5ĝ€̄μm, causing a discontinuity in the time series of &lt;i&gt;T&lt;/i&gt;12. To understand the impact of this change in the measured brightness temperatures, we have performed radiative transfer calculations for channel 12 of HIRS/2 and HIRS/3 instruments, using a large set of radiosonde profiles of temperature and relative humidity from three different sites. Other possible changes within the instrument, apart from the changed spectral response function, have been assumed to be of minor importance, and in fact, it was necessary to assume as a working hypothesis that the spectral and radiometric calibration of the two instruments did not change during the relatively short period of their common operation. For each radiosonde profile we performed two radiative transfer calculations, one using the HIRS/2 channel response function of NOAA 14 and one using the HIRS/3 channel response function of NOAA 15, resulting in negative differences of &lt;i&gt;T&lt;/i&gt;12 (denoted as &quot;&lt;i&gt;T&lt;/i&gt;12 :Combining double low line &lt;i&gt;T&lt;/i&gt;12ĝ•15 ĝ&apos; &lt;i&gt;T&lt;/i&gt;12ĝ•14) ranging between ĝ&apos;12 and ĝ&apos;2ĝ€̄K. Inspection of individual profiles for large, medium and small values of &quot;&lt;i&gt;T&lt;/i&gt;12 pointed to the role of the mid-tropospheric humidity. This guided us to investigate the relation between &quot;&lt;i&gt;T&lt;/i&gt;12 and the channel 11 brightness temperatures which are typically used to detect signals from the mid-troposphere. This allowed us to construct a correction for the HIRS/3 &lt;i&gt;T&lt;/i&gt;12, which leads to a pseudo-channel 12 brightness temperature as if a HIRS/2 instrument had measured it. By applying this correction we find an excellent agreement between the original HIRS/2 &lt;i&gt;T&lt;/i&gt;12 and the HIRS/3 data inferred from the correction method with &lt;i&gt;R&lt;/i&gt; Combining double low line 0.986. Upper-tropospheric humidity (UTH) derived from the pseudo HIRS/2 &lt;i&gt;T&lt;/i&gt;12 data compared well with that calculated from intersatellite-calibrated data, providing independent justification for using the two intercalibrated time series (HIRS/2 and HIRS/3) as a continuous HIRS time series for long-term UTH analyses. © Author(s) 2018