Intercomparisons of Long-Term Atmospheric Temperature and Humidity Profile Retrievals

Abstract

This study builds upon a framework to develop a climate data record of temperature and humidity profiles from high-resolution infrared radiation sounder (HIRS) clear-sky measurements. The resultant time series is a unique, long-term dataset (1978–2017). To validate this long-term dataset, evaluation of the stability of the intersatellite time series is coupled with intercomparisons with independent observation platforms as available in more recent years. Eleven pairs of satellites carrying the HIRS instrument with time periods that overlap are examined. Correlation coefficients were calculated for the retrieval of each atmospheric pressure level and for each satellite pair. More than 90% of the cases examining both temperature and humidity have correlation coefficients greater than 0.7. Very high correlation is demonstrated at the surface and two meter levels for both temperature (>0.99) and specific humidity (>0.93). For the period of 2006–2017, intercomparisons are performed with four independent observations platforms: radiosonde (RS92), constellation observing system for meteorology ionosphere and climate (COSMIC), global climate observing system (GCOS) reference upper-air network (GRUAN), and infrared atmospheric sounding interferometer (IASI). Very close matching of surface and two meter temperatures over a wide domain of values is depicted in all presented intercomparisons: intersatellite matches of HIRS retrievals, HIRS vs. GRUAN, and HIRS vs. IASI.

1. Introduction

Since 1978, the high-resolution infrared radiation sounder (HIRS) instruments have been making observations of the atmosphere from the surface to the stratosphere. The instruments are carried by both the National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellite series and the meteorological operational satellite program (Metop) A and B satellites operated by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). Among all satellite sounders, the HIRS observations have provided the longest period of record for measurements of global temperature and humidity.

The HIRS instrument has 20 channels, of which channels 1–12 are longwave channels ranging from 6.5–15 µm, channels 13–19 are shorter wavelengths between 3.7 and 4.6 µm, and channel 20 is a visible channel (0.69 µm). The sounder is a discrete stepping, line-scan instrument, with a nadir diameter of approximately 20 km for HIRS/2-3 instruments and 10 km for HIRS/4 (NOAA polar orbiter data user’s guide and NOAA KLM user’s guide, available at https://www1.ncdc.noaa.gov/pub/data/satellite/publications/podguides/). Together with other sounder measurements, HIRS data have been used in various applications. For example, an improved initialization inversion (3I) method was built based on physical–statistical algorithms [1] to retrieve meteorological variables from NOAA polar orbiting satellites. An international advanced television and infrared observation satellite operational vertical sounder (ATOVS) processing package (IAPP) was developed for real-time users to retrieve atmospheric temperature and moisture profiles and other parameters [2]. HIRS data were used to construct surface temperature data over both land [3,4] and sea [5]. HIRS derived upper tropospheric water vapor data were used as one of the datasets to study the feedback mechanism for amplifying anthropogenic climate change [6]. HIRS measurements supported other observations in examining the Walker circulations [7] and expansion of the tropical zone [8]. HIRS measurements were also used in studies of the El Niño–Southern Oscillation (ENSO) [9,10,11,12,13]. HIRS is one of the sounders processed in the NOAA product system [14].

Previous work involved preliminary algorithm development using HIRS to build an atmospheric profile dataset suitable for climate applications [15]. In that study, global comparisons with RS92 radiosonde and global positioning system radio occultation (GPS RO) observations showed mean biases within +/−0.3 °C for temperature and within +/−0.2 g/kg for specific humidity at standard pressure levels (850, 700, 600, 500, 400, 300, 200, 100, and 50 hPa).

Along with the homogeneous observations provided by RS92 radiosonde and GPS RO observations, several other atmospheric profile measurement platforms exist including: the high-quality measurements from radiosondes in the global climate observing system (GCOS) reference upper-air network (GRUAN) and retrievals based on the infrared atmospheric sounding interferometer (IASI) instrument. Observations from these four platforms are compared to the HIRS-based retrieval in this study. It should be noted that none of these available alternative data sets provide nearly the long-term time series available based on HIRS. However, assessing the quality of the HIRS retrieval in recent years where these platforms overlap in conjunction with an evaluation of the stability of the complete time series provides a long-term picture of the quality of the full dataset.

This study builds upon the initially presented framework [15]. In Section 2 we describe the use of HIRS observations for a long-term retrieval of atmospheric temperature and humidity profile data. We then examine the stability of the time series, which is based on input from twelve different satellite platforms, in Section 3.1. Finally, in Section 3.2, we present multiple intercomparisons as independent validation of the data set. The discussion and conclusion sections follow.

2. Materials and Methods

Much of the basis for the retrieval algorithm has been previously described [15]. We briefly summarize the approach in Section 2.1, highlighting in particular changes to the implementation applied herein. The independent data sets used for bias correction of the retrieval and comparison purposes presented in Section 3 are described in Section 2.2.

2.1. Retrieval Algorithm

The main components of the retrieval included: neural network training, cloud-screening, and bias calibration.

2.1.1. Neural Network Training

At higher elevations, a few channels, such as channels 6 and 7 that normally measure temperatures in the 700–1000 hPa layer, start to intersect the surface. To account for the influence of surface elevation, separate neural networks with inputs for HIRS channel data, emissivity, and CO₂ were trained and applied based on surface pressure to connect long-wave HIRS observations to atmospheric profile information. Using Metop-A as the reference satellite, limb-corrected HIRS data were inter-calibrated as a function of brightness temperatures to be used as input into a three-layer feed-forward neural network [16,17,18,19,20]. This approach also accounts for the spectral changes in the HIRS sensor during the period of record.

The neural networks were trained using data produced by a radiative transfer model, radiative transfer for TOVS (RTTOV) [21]. RTTOV was specifically developed to simulate satellite sounder measurements, including HIRS. More than 62,000 clear-sky ECMWF sampled profiles covering all latitudes and longitudes provided the input to RTTOV, which output the simulated HIRS channel brightness temperatures for Metop-A’s HIRS instrument.

Due to the impact of CO₂ on brightness temperatures [15], neural networks were trained with profile data produced under six different RTTOV modeling scenarios: 330, 350, 370, 390, 410, and 430 ppm. Monthly Mauna Loa observations were used as an input of the retrieval [22]. Surface emissivity has been shown to have an impact on surface skin and air temperatures. Surface emissivity values as defined in the International Satellite Cloud Climatology Project (ISCCP) dataset were used as an input of the retrieval for surface and near-surface variables [23].

2.1.2. Cloud-Screening

As an infrared sounder, HIRS can only sense the top of clouds when clouds are present. Therefore, the temperature and humidity profile retrievals are based only on clear-sky pixels. A two-tiered approach was used to identify cloudy pixels. The first pass, which filters the majority of clouds, was based on a cloud detection procedure used in ISCCP [24]. The second procedure used optimized cloud fraction and cloud probability values from the AVHRR pathfinder atmospheres-extended (PATMOS-x) climate data record (CDR) to identify remaining cloudy pixels [25].

2.1.3. Bias Calibration

GPS RO derivations yielded stratospheric temperatures with high accuracy while spatially and temporally homogenous radiosonde observations provided highly accurate tropospheric temperature and humidity data. These two platforms were used in combination for bias correction. GPS RO (specifically, COSMIC2013) data from 2008–2010 was used for correction of the HIRS temperature data for the stratosphere (50–300 hPa levels). Global RS92 data from 2008–2010 was used for correction of the remaining pressure levels for temperature data and all pressure levels for humidity data.

HIRS pixels determined as clear or only possibly partially cloudy were matched to COSMIC2013 and RS92 data within 0.1° latitude and longitude and one hour of measurement at each pressure level. A multiple linear regression was performed for each pressure level with dependencies on latitude and the HIRS observed values. Further details may be found in [15].

2.1.4. Significant Changes from Previous Studies

In all, these changes detailed below have resulted in an improved dataset from previous versions. By removing inputs deemed unstable, the resulting retrieval is more consistent as a long-term data record. In comparison to validation results shown with the previous version in [15], the agreement of the HIRS retrieval with COSMIC and RS92 in recent years appears to be similar if not slightly more in agreement.

Removed channel 10 as a neural network input: based on the conclusions of channel 10 instability over the long-term, this version did not include channel 10 as an input [19]. Temperature retrievals for standard pressure levels from 1000 hPa to 50 hPa used channels 2–9 and 11–12 (in addition to CO₂ inputs). Humidity retrievals for standard pressure levels from 1000 hPa to 300 hPa used channels 4–8 and 11–12 (in addition to CO₂ inputs). Surface skin and 2 m temperature retrievals used channels 7–8 (in addition to CO₂ and emissivity inputs). Two meter humidity retrievals used channels 7–8 and 11 (in addition to CO₂ and emissivity inputs).

Removed unreliable Metop-02 data: analysis revealed instability in HIRS measurements on Metop-02 beginning May 2011 and lasting through March 2013. Figure 1 illustrates the instability. These data have been omitted as inputs to the retrieval algorithm as any derived data would be suspect. Most of this time period was covered by NOAA-17, so the omission did not adversely impact the overall time series continuity.

Using only two neural networks instead of three: separate neural networks were developed for groupings by surface pressure. Previously, there were three groups of surface pressure (Ps): Ps < 700 hPa, 700 hPa ≤ Ps < 850 hPa, and Ps ≥ 850 hPa. Because the vast majority of the globe has Ps ≥ 850 hPa, we simplified the groupings to two: Ps < 850 hPa and Ps ≥ 850 hPa.

Bias correction procedure: no bias correction was performed to surface skin or air measurements as no apparent systematic biases requiring correction were noted. In the previous version of this dataset, corrections were made to profile observations with RS92 (temperature 1000–400 hPa; humidity all 1000–300 hPa) and GPS RO data (specifically, COSMIC2013 data for temperature 300–50 hPa) for 2 years: 2011–2012. Due to the detection and subsequent removal of unreliable Metop-02 data during 2011–2013, in this latest version, three years of data were used: 2008–2010.

Extreme outlier removal: after the bias correction procedure, it was seen that some values may be adjusted to infeasible extreme values. To mitigate against this possibility, a method was devised to remove possible outliers for levels 1000 hPa and above (that is, surface and 2 m data remain unchanged). To establish the bounds for reasonable data values, all available RS92 data for 2007–2014 was compiled. Minimum and maximum temperature bounds per standard level were identified as the mean ±2.5 standard deviations. Minimum and maximum specific humidity bounds were identified as the mean ±1.5 standard deviations. Additionally, the ECMWF training data set was examined to ensure all training data fell within these limits established by RS92 data, and if not, the bounds were adjusted to contain the extremes of the training data set as well. The fraction of extreme outliers from the HIRS retrieval excluded by this methodology amounts to less than 1% of temperature data and less than 0.7% of humidity data.

Additional quality control flags: there was a quality flag indicating (0) clear, (1) possibility of partially cloudiness, (2) likely cloudy, and (3) no cloud fraction/probability information available. Additionally, in this version, a quality flag has been added to indicate where humidity inversion (nonmonotonic decrease with altitude) was present, and if an adjustment was made. In particular the quality flag indicates (0) uncorrected monotonic humidity decrease with altitude, (1) uncorrected humidity inversion, and (2) corrected humidity inversion. If humidity inversion was detected, it was corrected if the location is non-polar (considered here as latitude below/above −60/60° N). If the location was polar it was left as it was. The assumption here was that humidity inversion was possible over the poles but typically not over the rest of the globe. Any correction applied to 2 m values was assigned the humidity closest to the surface. Other profile corrections were assigned the humidity at the adjacent standard level closer to the surface.

2.2. Data

2.2.1. RS92

Global RS92 data [26] from 2008–2010 was used for bias correction of the HIRS data, as described in Section 2.1.3. Data from 2013–2017 was used for validation purposes in Section 3.2.1. During this intercomparison effort, outlier RS92 temperature values were noted. To filter out the rogue RS92 outliers, we used the limiting bounds established with the RS92 and training data, as described in Section 2.1.4.

2.2.2. COSMIC

GPS RO measurements from Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) were used both as part of the retrieval process and for validation purposes. Two versions of COSMIC data were used in this study: the original COSMIC as well as COSMIC2013, the re-processed version of GPS RO derived profiles. COSMIC and COSMIC2013 data were obtained from University Corporation for Atmospheric Research COSMIC Data Analysis and Archive Center (CDAAC) [27]. Previous studies comparing COSMIC and multiple types of radiosonde temperature measurements in the upper troposphere and lower stratosphere indicate that RS92 agrees well with a mean bias close to zero [28].

COSMIC2013 data from 2008–2010 was used for bias correction of the HIRS temperature data for the stratosphere (50–300 hPa levels), as detailed in Section 2.1.3. Global COSMIC2013 data from 2013-2014 and COSMIC data from 2014–2017 was used for validation purposes.

2.2.3. GRUAN

The GCOS GRUAN measurements included high-quality RS92 measurements for the surface to the stratosphere (https://www.gruan.org). Data from eight sites located across the globe with observations ranging from 2006–2017 were used for validation purposes [29]. Note that although GRUAN uses RS92 radiosondes, they apply their own unique calibration procedure and this data was not a subset of the RS92 data described in Section 2.2.1.

2.2.4. IASI

On board the MetOp series of polar orbiting satellites (including MetOp-02, which also carries a HIRS instrument) is the IASI [30]. This hyperspectral instrument was designed to measure temperature and humidity profiles in the troposphere and lower stratosphere, and be the successor to HIRS in this regard. Given that both instruments are on the same satellite, the number of matches was substantial. To examine any seasonality in comparisons, both January and July observations were examined for 2014.

Level 2 IASI products included profile observations of both temperature and atmospheric water vapor approximate to the standard levels of the HIRS product, as well as surface skin temperatures. Previous studies, limited to regional analysis, evaluating the IASI temperature and moisture retrievals as compared to radiosonde observations have shown consistent behavior with the exception of near surface temperatures over land and humidity values under dry atmospheric conditions [31,32,33].

3. Results

The consistency of the retrieval, as determined through intersatellite comparison, is examined in Section 3.1. Independent validation and intercomparisons with five different datasets are presented in Section 3.2.

3.1. Intersatellite Comparisons

Twelve polar orbiting satellites with the HIRS instrument were used to produce the retrievals: N-6, N-7, N-8, N-9, N-10, N-11, N-12, N-14, N-15, N-16, N-17, and Metop-2. The first eleven items of this list are from the operational NOAA polar orbiting series. The last satellite is from the meteorological operational satellite program (Metop) series operated by EUMETSAT. From this list, there are eleven pairs of satellites with time periods that overlap.

Table 1. Eleven satellite pairs with temporal overlap. Satellite pairs, date range of overlap, number of profile matchups, and coverage characteristics are described. Data were considered a match if within 0.02° latitude and longitude and one hour of measurement.

Satellite Pair	Date Range	No. Profile Matchups	Coverage 1
N-7/N-8	16 May 1983–30 May 1984	1117	polar
N-9/N-10	17 Nov 1987–18 Oct 1988	1629	polar
N-10/N-11	8 Nov 1988–16 Sept 1990	3147	polar
N-11/N-12	16 Sept 1991–27 Aug 1994	6597	polar
N-12/N-14	9 Feb 1995–31 March 1997	3788	polar
N-14/N-15	31 Oct 1998–25 July 2002	31,826	global
N-14/N-16	20 Mar 2001–25 July 2002	3491	polar
N-15/N-16	20 Mar 2001–26 Oct 2003	6452	polar
N-15/N-17	24 Aug 2002–30 Mar 2005	6864	polar
N-16/N-17	24 Aug 2002–31 Dec 2003	9049	polar
M-02/N-17	29 June 2007–10 Jan 2009	211,176	global

¹ Coverage is considered “polar” if the majority of matchups occur above 60° N or below −60° N.

summarizes the associated matchups where data were within 0.02° latitude and longitude and one hour of measurement.

Correlation coefficients were calculated for each atmospheric pressure level and for each satellite pair. Figure 2 illustrates the correlation coefficients which may be interpreted as a measure of the agreement between the two set of observations. When evaluating all cases for both temperature and humidity (11 satellite pairs × (8 humidity levels + 12 temperature levels) = 220 cases), correlation coefficients greater than 0.7 was achieved more than 90% of the time.

Where the correlation coefficients were below 0.7, the comparisons between temperature retrievals showed two satellite pairs with poorer correlations at the 500 hPa level and eight satellite pairs at the 300 hPa level. There were two satellite pairs that had sufficient agreement for temperature at all levels, N-14/N-15 and M-02/N-17, which were particularly significant as they were the two comparisons with globally located matchups. There were six satellite pairs that had sufficient agreement for specific humidity at all levels. The remaining five satellite pairs had correlation coefficients below 0.7 at 500 hPa (N-7/N-8, N-9/N-10, N-14/N-15), 400 hPa (N-7/N-8, N-9/N-10, N-11/N-12, N-12/N-14, N-14/N-15), and 300 hPa (N-9/N-10, N-11/N-12, N-14/N-15). Normalized histograms of a sample of these results are presented in Figure 3. For this figure, three representative satellite pairs were selected: N-9/N-10 to show a case where matches were only in the polar region, N-14/N-15, and M-02/N-17 to show the cases where matches occurred globally. The particular levels were chosen to visualize cases of both sufficient and insufficient intersatellite correlation.

Of particular note was the very high correlation at the surface and 2 m levels for both temperature and specific humidity. This was of interest because the retrieval at these levels uses only HIRS data from Channels 7–8 for temperature and Channels 7–8 and 11 for specific humidity. Conversely, the profile retrievals used ten and seven HIRS channels as inputs for temperature and specific humidity, respectively.

3.2. Independent Validation

In this section, comparisons were made only to data derived from Metop-2 HIRS observations.

Table 2. Pressure levels where comparisons with high-resolution infrared radiation sounder (HIRS) retrievals are available and performed. T and Q indicate temperature and specific humidity comparisons, respectively.

Data set	Surface	2 m	1000 hPa	850 hPa	700 hPa	600 hPa	500 hPa	400 hPa	300 hPa	200 hPa	100 hPa	50 hPa
RS92			T,Q	T,Q	T,Q	T,Q	T,Q	T,Q	T,Q	T	T	T
COSMIC 1			T,Q	T,Q	T,Q	T,Q	T,Q	T,Q	T,Q	T	T	T
GRUAN		T,Q	T,Q	T,Q	T,Q	T,Q	T,Q	T,Q	T,Q	T	T	T
IASI	T		T,Q	T,Q	T,Q	T,Q	T,Q	T,Q	T,Q	T	T	T

¹ COSMIC and COSMIC2013 comparisons are at the same levels.

summarizes the pressure levels where comparisons were made with the independent data sets. In general, data were considered a match if within 0.1° latitude and longitude and one hour of measurement, the HIRS cloud flag was 0 or 1 (clear or possibility of partially cloudiness), and the HIRS data were not corrected for temperature inversion.

3.2.1. RS92

RS92 provides water vapor pressure values which were transformed to specific humidity for comparison with HIRS-based retrievals. Results from matches between 2013–2017 are presented in Figure 4a,d where there were at least 10 matchups for a pressure level. Global mean bias errors (MBE), where MBE = HIRS_value − other_value, averaged across all pressure levels from 1000 hPa to 50 hPa is close to zero for temperature (0.0005 °C). Similar global MBE averaged for humidity were 0.12 g/kg, the magnitude of which was driven by larger errors seen at the 850 hPa level.

3.2.2. COSMIC and COSMIC2013

COSMIC and COSMIC2013 data flagged with quality control issues were not used. COSMIC and COSMIC2013 provide water vapor pressure values which were transformed to specific humidity for comparison with HIRS. Results for where there were at least 10 matchups for a pressure level are presented in Figure 4b,c,e,f. Data from 2014–2017 was matched with COSMIC and data from 2013–2014 was matched with the reprocessed COSMIC2013.

High accuracy was assumed for these GPS RO observations of stratospheric temperatures, which is why COSMIC2013 data were used for bias correction of 50–300 hPa temperature retrievals. The comparisons with HIRS-based retrievals do match closer at these levels than in the troposphere, albeit measurements in the southern-most latitudinal band exhibits notable variability for both COSMIC and COSMIC2013. For COSMIC, global MBE averaged across all pressure levels from 1000 hPa to 50 hPa was 0.18 °C for temperature. Similar global MBE averaged for humidity were 0.49 g/kg, the magnitude of which was driven by larger errors seen at the 850 hPa level. For COSMIC2013, it was 0.33 °C for temperature and 0.31 g/kg for humidity. In general, the GPS RO humidity observations at 700–1000 hPa appeared to be significantly lower than the HIRS-based retrievals.

3.2.3. GRUAN

Launch location and time was used for matching of 2 m temperature and humidity observations (although GRUAN refers to these as surface observations). GRUAN pressure levels nearest to the standard HIRS pressure levels were matched with actual latitude and longitude locations, incorporating drift for profile temperature and humidity observations. GRUAN provides relative humidity values which were transformed to specific humidity for comparison with HIRS-based retrievals. Specific GRUAN sites used for comparisons and the number of matches at each are indicated in

Table 3. Reference upper-air network (GRUAN) sites and locations. The number of matches count each pressure level independently, for observations between 2006 and 2017.

GRUAN Site	Latitude (°N)/Longitude (°E)	Surface Elevation (m)	No. Matches
Barrow 1	71.32/−156.61	8	798
Lauder	−45.05/169.68	370	10
Lindenberg	52.21/14.12	98	403
Ny-Ålesund	78.92/11.93	5	525
SGP 1	36.60/−97.49	320	57
Sodankylä	67.37/26.63	179	53
Tateno	36.06/140.13	27	129
Tenerife 1	28.32/−16.38	115	311

¹ These sites are not yet fully certified by GRUAN.

. Comparison results are presented in Figure 5, where there were at least 10 matchups for a pressure level.

In Figure 5a, the Ny−Ålesund station is an outlier root mean square error (RMSE) at both the 2 m and 1000 hPa levels, but this may be an artifact of the matchup there because the surface pressure is very close to 1000 hPa. The outlier at 400 hPa is the Tateno station, where HIRS is slightly overestimating temperature. Similarly, the outlier at 300 hPa is from the Barrow station where HIRS is slightly underestimating temperature.

Overall, in Figure 5c there are many levels showing coverage of zero MBE, indicative of good temperature estimation with HIRS. Both the 1000 hPa and 850 hPa levels show all matched stations have negative MBEs, indicating an underestimation by HIRS. Conversely, the 700, 600 and 500 hPa levels had primarily positive MBEs, indicating an overestimation by HIRS. The outliers occurring at 850 hPa (SGP) and 700 hPa (Tenerife) showed a better agreement with HIRS as the values are closer to zero. However, the outlier at the 600 hPa level, Ny-Ålesund, is more positive indicating that HIRS may be especially overestimating temperature at this level for this station.

The specific humidity RMSEs shown in Figure 5b are consistently decreasing with increasing altitude. Further, as shown in Figure 5d, all levels show coveraged of zero MBE, indicating good agreement between HIRS and the GRUAN measurements.

3.2.4. IASI

For each of the two months examined, January and July of 2014, there were more than 50,000 matches at the surface and for each standard pressure level with the exception of 1000 hPa where there were on the order of 16,000 matches in July and 24,000 in January due to matches in high terrains where the surface pressure was less than 1000 hPa. Data were considered a match if within 0.05° latitude and longitude of the same orbit, the HIRS cloud flag was 0 or 1, and the HIRS data were not corrected for temperature inversion. IASI pressure levels nearest to the standard HIRS pressure levels were matched, ultimately within 3% of the standard levels.

HIRS specific humidity was compared directly to IASI atmospheric water vapor. Figure 6c,d show that the humidity comparisons are fairly similar in both January and July, although the global MBEs are slightly lower across all pressure levels in July.

The temperature comparisons are more seasonally complex. At the 100 and 50 hPa levels in July the global MBEs are negative versus positive in January (Figure 6a,b). The average MBE for all latitudinal bands examined were negative in July, indicating that either HIRS is too cool or IASI is too warm at these levels during the summer. In January the global response was mixed, with positive MBEs along the equator and both poles at 100 and 50 hPa, but overall much better matching between HIRS and IASI. The near surface levels (1000 and 850 hPa) appear to have much better MBEs in July, particularly for the 30–90°N latitude region. So, in the winter HIRS is too warm (or IASI is too cool), but the estimates seem closer in July.

4. Discussion

The results presented from the intersatellite comparison yield insight into both the quality of the retrieval at different levels as well as the quality of the intersatellite calibration performed external to this study. Sufficient agreement between retrievals on separate but overlapping satellites, as measured by correlation coefficients being greater than 0.7, is achieved for more than 90% of the cases for both temperature and humidity. Some differences are expected because the matches are not exact in space and time (within 0.02 degrees latitude/longitude and one hour). Weak correlations can be a result of either poor calibration for some or all HIRS channels, or a poor retrieval algorithm. If all inputs were not calibrated well, all retrieval levels would exhibit poor correlation. This is not the case here. In particular extremely high correlation is found in all pairs for surface and 2 m observations. These retrievals are based on channels 7, 8, and 11, and it is fair to conclude these channels are well-calibrated. However, weak correlation is seen for several satellite pairs at varying pressure levels. Since the profile retrievals include inputs from a wider range of HIRS channels (2–9, 11–12), we conclude that some of these channels have room for improvement in their calibration and that the retrievals are more sensitive to different channels at different pressure levels.

In Table 1, it is indicated if the intersatellite matches occur globally or only in the polar region. The “polar-only” matching occurs because the satellite pair is measuring at different times of the day, and therefore the orbits do not meet the matching requirements of within one hour except in the polar region. This is an important consideration because, in general, the pairs with only polar matches have both fewer matchups and a much smaller range of values. The very dry conditions over the poles especially limits the range of specific humidity values in the “polar-only” matched pairs (e.g., Figure 3 shows a representative polar matchup in Figure 3d and global matching in Figure 3e,f). There is more variability relative to the lower specific humidity values, leading to lower calculated correlations in the cases where matchups are not global.

By comparing correlation coefficients amongst these satellite pairs, we are assuming that conditions are fairly similar during these different time periods of overlap. However, in Figure 3b,c, both pairs which tout global matchups, the normalized distributions of values look quite different although both are well-correlated. It is possible that it may not be fair to directly compare the correlations seen between, for example, N-7/N-8 in 1983 to M-02/N-17 in 2008 when very different environmental conditions could be prevailing. This situation is unlikely, but is something to consider when interpreting the results presented in Figure 2.

GPS RO humidity observations at 700–1000 hPa are too dry given the results of COSMIC2013 compared to RS92, which has more reliability in the troposphere. GPS RO humidity observations at 700–1000 hPa are significantly lower than the HIRS-based retrievals, with the exception of the polar regions (Figure 4e,f). However, HIRS retrievals compare fairly well with RS92 at these same levels (Figure 4d). Given that RS92 is assumed to have high accuracy in the troposphere, this suggests that both of these GPS RO humidity datasets may be too dry at the 700–1000 hPa levels.

Global MBE of humidity data is significantly higher in IASI in the 700–1000 hPa levels. Given the reliability of RS92, and how well HIRS-based retrievals match (Figure 4d), this suggests that IASI is possibly too dry at these levels. The phenomenon is similar for both January and July, although the effect appears to be greater in January. The globally averaged dryness seems to be driven by the region between −30 and 60° N in January and the region between −60 and 30° N in July.

Temperature comparisons with IASI (Figure 6a,b) have much more variability than comparisons with RS92 (Figure 4a) across all latitudinal bands. RS92 MBEs at all levels seem to be well contained within a one-degree margin, whereas IASI MBEs vary from nearly −4 to +6 degrees. Focusing on the global average MBE, IASI is cooler than HIRS at all levels in January. Triangulating these results with the RS92 comparisons, it suggests that the IASI retrieval may be too cool at all studied levels in January. The results in July are slightly more complex. All latitudinal bands show a negative MBE at the 50–100 hPa levels. Comparing to the good agreement between RS92 and HIRS, this suggests that IASI may be too warm at these levels in July, especially in the Northern Hemisphere.

Perhaps the most striking intercomparisons of this study are those of surface and two meter temperatures. The intersatellite correlation of both of these measurements was extremely high in all matched pairs (Figure 2a). Further, examining the scatterplots show great agreement for a large range of possible values. Figure 7a presents, as an example, the global matchups of two meter temperatures between Metop-2 and NOAA-17. Results for the surface temperatures and for the other 10 pairs are similar. Of particular note is the continued close agreement found in the extreme values. Table 2 indicates that GRUAN measured (approximate) two meter temperatures and IASI measured surface temperatures. The GRUAN sites are geographically diverse and provide a wide range of observations (Figure 7c). Surface temperatures from IASI matched to HIRS-based retrievals in January 2014 are shown in Figure 7b. Despite the variability of the comparisons at other levels, the surface temperatures are remarkably aligned.

5. Conclusions

This study builds upon the initially presented framework to develop a climate data record of temperature and humidity profiles from HIRS clear-sky measurements [15]. The resultant time series is a unique, long-term dataset (1978–2017). Although important as a stand-alone data record, this time series also serves as a critical input for several other climate data sets including: ISCCP [23] and the NASA/GEWEX Surface Radiation Budget [34]. Both of these reach thousands of users, so the downstream impact of this input is significant.

Updates which improve the quality of the retrieval algorithm have been described. To validate this long-term dataset, evaluation of the stability of the intersatellite time series is coupled with intercomparisons with independent observation platforms as available in more recent years. Previous, current, and future efforts are all in support of developing a Climate Data Record of temperature and humidity profiles from HIRS clear-sky measurements.

Correlation coefficients greater than 0.7 is achieved for more than 90% of the eleven different intersatellite overlapping cases for both temperature and humidity. However, it is noted that some of the channels have room for improvement in their calibration and that the retrievals are more sensitive to different channels at different pressure levels, a potential area for future research.

Evaluation of the surface and two meter temperature retrievals demonstrate universally high consistency between all satellite pairs as well as with independent data platforms. The retrieval for these variables is dependent on only two HIRS channels while profile retrievals use 10 HIRS channels. This may suggest one of two possibilities: (1) these two channels are incredibly well-suited for describing the surface temperature while no combination of channels can describe the profile temperatures as well, or (2) using fewer and more specific channels is a better methodology for each individual profile level. Exploring these possibilities is another potential area for future research.