Observed and Modeled Mountain Waves from the Surface to the Mesosphere Near the Drake Passage

Four state-of-the-science numerical weather prediction (NWP) models were used to perform mountain wave- (MW) resolving hind-casts over the Drake Passage of a 10-day period in 2010 with numerous observed MW cases. The Integrated Forecast System (IFS) and the Icosahedral Nonhydrostatic (ICON) model were run at Δx ≈ 9 and 13 km globally. TheWeather Research and Forecasting (WRF) model and the Met Office Unified Model (UM) were both configured with a Δx = 3 km regional domain. All domains had tops near 1 Pa (z ≈ 80 km). These deep domains allowed quantitative validation against Atmospheric InfraRed Sounder (AIRS) observations, accounting for observation time, viewing geometry, and radiative transfer. All models reproduced observed middle-atmosphere MWs with remarkable skill. Increased horizontal resolution improved validations. Still, all models underrepresented observed MW amplitudes, even after accounting for model effective resolution and instrument noise, suggesting even at Δx ≈ 3 km resolution, small-scale MWs are under-resolved and/or over-diffused. MWdrag parameterizations are still necessary in NWP models at current operational resolutions of Δx ≈ 10 km. Upper GW sponge layers in the operationally configured models significantly, artificially reduced MW amplitudes in the upper stratosphere and mesosphere. In the IFS, parameterized GW drags partly compensated this deficiency, but still, total drags were ≈ 6 time smaller than that resolved at Δx ≈ 3 km. Meridionally propagating MWs significantly enhance zonal drag over the Drake Passage. Interestingly, drag associated with meridional fluxes of zonal momentum (i.e. ) were important; not accounting for these terms results in a drag in the wrong direction at and below the polar night jet.

Technology of AIRS/AMSU/ATMS satellite data processing

The Atmospheric Infrared Sounder (AIRS) is a hyperspectral instrument with 2378 channels. It is a part of the Aqua space platform equipment. It registers outgoing longwave radiation in the IR-band from 3.74 to 15.4 microns. To correctly retrieve the atmospheric profiles in the presence of cloud structures, AIRS measurement processing scheme uses data from the 15-channel AMSU-A microwave instrument, which is also installed on Aqua. The paper proposes a technology for synthesizing the readings of AMSU-A, that failed in the fall of 2015, by using observations from the 22-channel radiometer of the Advanced Technology Microwave Sounder (ATMS) installed on Suomi-NPP and NOAA-20 satellites. These platforms were launched in 2011 and 2018, respectively. The transition between the coordinate grids of the two instruments was implemented by the “resample” software library, which transferred the radiance temperature values obtained by ATMS radiometer to the AMSU-A irregular measurement grid by means of a Gaussian function. The method was tested for the Aqua and Suomi-NPP neighboring orbits of 2015, when AMSU-A was still operating normally. It is established that the root-mean-square deviation of the radiance temperature values during the transferring of ATMS data to the AMSU-A coordinate grid does not exceed 1%, and the correlation coefficient is 0.98. Using the synthesized AMSU-A readings, reconstructions of the parameters of the atmosphere and the underlying surface were carried out. The analysis of the obtained results showed the suitability of the proposed method of replacing the microwave data from AMSU by the data from ATMS instruments. It should be noted that in the case of a rapidly changing atmosphere, for example, with a strong wind, the use of ATMS observations is possible only if the difference in the passage time of the two satellites does not exceed 10–15 minutes.

Improved estimation of volcanic SO₂ injections from satellite observations and Lagrangian transport simulations: the 2019 Raikoke eruption

Monitoring and modeling of volcanic plumes is important for understanding the impact of volcanic activity on climate and for practical concerns, such as aviation safety or public health. Here, we applied the Lagrangian transport model Massive-Parallel Trajectory Calculations (MPTRAC) to estimate the SO2 injections into the upper troposphere and lower stratosphere by the eruption of the Raikoke volcano (48.29° N, 153.25° E) in June 2019 and its subsequent long-range transport and dispersion. First, we used SO2 observations from the AIRS (Atmospheric Infrared Sounder) and TROPOMI (TROPOspheric Monitoring Instrument) satellite instruments together with a backward trajectory approach to estimate the altitude-resolved SO2 injection time series. Second, we applied a scaling factor to the initial estimate of the SO2 mass and added an exponential decay to simulate the time evolution of the total SO2 mass. By comparing the estimated SO2 mass and the observed mass from TROPOMI, we show that the volcano injected 2.1 ± 0.2 Tg SO2 and the e-folding lifetime of the SO2 was about 13 to 17 days. The reconstructed injection time series are consistent between the AIRS nighttime and the TROPOMI daytime measurements. Further, we compared forward transport simulations that were initialized by AIRS and TROPOMI satellite observations with a constant SO2 injection rate. The results show that the modeled SO2 change, driven by chemical reactions, captures the SO2 mass variations observed by TROPOMI. In addition, the forward simulations reproduce the SO2 distributions in the first ~10 days after the eruption. However, diffusion in the forward simulations is too strong to capture the internal structure of the SO2 clouds, which is further quantified in the simulation of the compact SO2 cloud from late July to early August. Our study demonstrates the potential of using combined nadir satellite observations and Lagrangian transport simulations to further improve SO2 time- and height-resolved injection estimates of volcanic eruptions.

Validation and Error Estimation of AIRS MUSES CO Profiles with HIPPO, ATom and NOAA GML Aircraft Observations

Single footprint retrievals of carbon monoxide from the Atmospheric Infrared Sounder (AIRS) are evaluated using aircraft in situ observations. The aircraft data are from the HIAPER Pole-to-Pole (HIPPO, 2009–2011), the first three Atmospheric Tomography Mission (ATom, 2016–2017) campaigns and the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) Global Greenhouse Gas Reference Network Aircraft Program from 2006–2017. The retrievals are obtained using an optimal estimation approach within the MUlti-SpEctra, MUlti-SpEcies, MUlti-Sensors (MUSES) algorithm. Retrieval biases and estimated errors are evaluated across a range of latitudes from the sub-polar to tropical regions over both ocean and land points. AIRS MUSES CO profiles were compared with HIPPO, ATom, and NOAA GML aircraft observations with a coincidence of 9 hours and 50 km to estimate retrieval biases and standard deviations. Comparisons were done for different pressure levels and column averages, latitudes, day, night, land, and ocean observations. We find mean biases of +6.6 % +/− 4.6 %, +0.6 % +/− 3.2 %, −6.1 % +/− 3.0 %, and 1.4 % +/− 3.6 %, for 750 hPa, 510 hPa, 287 hPa, and the column averages, respectively. The mean standard deviation is 15 %, 11 %, 12 %, and 9 % at these same pressure levels, respectively. Observation errors (theoretical errors) from the retrievals were found to be broadly consistent in magnitude with those estimated empirically from ensembles of satellite aircraft comparisons. The GML Aircraft Program comparisons generally had higher standard deviations and biases than the HIPPO and ATom comparisons. Since the GML aircraft flights do not go as high as the HIPPO and ATom flights, results from these GML comparisons are more sensitive to the choice of method for extrapolation of the aircraft profile above the uppermost measurement altitude. The AIRS retrieval performance shows little sensitivity to surface type (land or ocean) or day or night but some sensitivity to latitude. Comparisons to the NOAA GML set spanning the years 2006–2017 show that the AIRS retrievals are able to capture the distinct seasonal cycles but show a high bias of ~20 % in the lower troposphere during the summer when observed CO mixing ratios are at annual minimum values. The retrieval bias drift was examined over the same period and found to be small at < 0.5 % over the 2006–2017 time period.

Spatiotemporal changes in atmospheric water vapor content and analysis of meteorological factors over the Tibetan Plateau and its surroundings

Based on radiosonde stations and V3.0 data, Atmospheric Infrared Sounder (AIRS)-only, Tropical Rainfall Measuring Mission satellite (TRMM) and MERRA2, and ERA-5 data, we evaluated the ability of each dataset to reproduce water vapor content and explored its relationship with precipitation and temperature over the Tibetan Plateau and its surroundings. The results showed that the southern part of the surrounding area had high water vapor content and a low water vapor content zone appeared in the inner part of the Tibetan Plateau. The largest water vapor content appeared in summer and the smallest in winter. Most of the products could capture the spatial distribution of water vapor content, ERA-5 had the smallest bias and the highest correlation coefficient with the radiosonde data. The water vapor content has shown a gradually increasing trend over the last 50 years, with the most obvious increase in summer. Several sets of products had the same fluctuation trend and value is greater than the radiosonde data. There was a significant positive correlation between air temperature and water vapor content in the Tibetan Plateau, especially in the south. As the latitude increased, the correlation between precipitation and water vapor content gradually decreased and a negative correlation appeared.

Progress toward Cloud-Cleared Infrared radiance assimilation in a global modeling framework: Application to the 2017 Atlantic Tropical Cyclone Season.

&lt;p&gt;Previous work by this team has demonstrated that assimilation of IR radiances in partially cloudy regions is beneficial to numerical weather predictions (NWPs), improving the representation of tropical cyclones (TCs) in global analyses and forecasts. The specific technique used by this team is based on the &amp;#8220;cloud-clearing CC&amp;#8221; methodology. Cloud-cleared hyperspectral IR radiances (CCRs), if thinned more aggressively than clear-sky radiances, have shown a strong impact on the analyzed representation and structure of TCs. However, the use of CCRs in an operational context is limited by 1) latency; and 2) external dependencies present in the original cloud-clearing algorithm. In this study, the Atmospheric InfraRed Sounder (AIRS) CC algorithm was (a) ported to NASA high end computing resources (HEC), (b) deprived of external dependencies, and (c) parallelized improving the processing by a factor of 70. The revised AIRS CC algorithm is now customizable, allowing user&amp;#8217;s choice of channel selection, user&amp;#8217;s model's fields as first guess, and could perform in real time. This study examines the benefits achieved when assimilating CCRs using the NASA&amp;#8217;s Goddard Earth Observing System (GEOS) hybrid 4DEnVar system. The focus is on the 2017 Atlantic hurricane season with three infamous hurricanes (Harvey, Irma, and Maria) investigated in depth.&amp;#160; The impact of assimilating customized CCRs on the analyzed representation of tropical cyclone horizontal and vertical structure and on forecast skill is discussed.&lt;/p&gt;

Verification of the Atmospheric Infrared Sounder (AIRS) and the Microwave Limb Sounder (MLS) ozone algorithms based on retrieved daytime and night-time ozone

Ozone (O3) plays a significant role in weather and climate on regional to global spatial scales. Most studies on the variability in the total column of O3 (TCO) are typically carried out using daytime data. Based on knowledge of the chemistry and transport of O3, significant deviations between daytime and night-time O3 are only expected either in the planetary boundary layer (PBL) or high in the stratosphere or mesosphere, with little effect on the TCO. Hence, we expect the daytime and night-time TCO to be very similar. However, a detailed evaluation of satellite measurements of daytime and night-time TCO is still lacking, despite the existence of long-term records of both. Thus, comparing daytime and night-time TCOs provides a novel approach to verifying the retrieval algorithms of instruments such as the Atmospheric Infrared Sounder (AIRS) and the Microwave Limb Sounder (MLS). In addition, such a comparison also helps to assess the value of night-time TCO for scientific research. Applying this verification on the AIRS and the MLS data, we identified inconsistencies in observations of O3 from both satellite instruments. For AIRS, daytime–night-time differences were found over oceans resembling cloud cover patterns and over land, mostly over dry land areas, which is likely related to infrared surface emissivity. These differences point to issues with the representation of both processes in the AIRS retrieval algorithm. For MLS, a major issue was identified with the “ascending–descending” orbit flag, used to discriminate night-time and daytime MLS measurements. Disregarding this issue, MLS day–night differences were significantly smaller than AIRS day–night differences, providing additional support for the retrieval method origin of AIRS in stratospheric column ozone (SCO) day–night differences. MLS day–night differences are dominated by the upper-stratospheric and mesospheric diurnal O3 cycle. These results provide useful information for improving infrared O3 products.

Air Pollutants During COVID-19 Lockdown Period in India

Recent studies have indicated that certain atmospheric pollutants had significantly reduced in several countries during the lockdown period imposed to curb the spread of SARS-CoV-2-Virus. The Government of India declared the first lockdown from the end of March 2020, which continued till June 2020 in most Indian states. The present study compares the air quality indicators nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) across India, during the months of March–August 2020 and the same period in 2019. The application of satellite information from NASA – Ozone Monitoring Instrument and Atmospheric Infrared Sounder were used to compare the quantum of air pollutants. The temporal variation of the air pollutants was studied using satellite imagery and geo-statistics on a monthly, national average basis, to assess the overall impact of the lockdown. NO2, SO2, and O3 showed some level of reduction during the period of study in 2020 when compared to 2019, whereas CO levels had gone up in 2020. NO2, a pollutant mainly arising from motor vehicle combustion, reduced by 3.98–12.1% in 2020 as compared to the same study period in 2019 and in April 2020, when there was a complete lockdown, it had dropped maximally (by 12.1%). The reduction in SO2 levels in 2020 ranged from around 0.5–9% but only during April–June 2020, whereas there was an increase in March, July, and August 2020 when compared to 2019. Despite a reduction in NO2, the O3 levels (which are dependent on NO2 levels) saw an increase in the atmosphere during March–May 2020 by 1.9–5%, and decreased during June–August 2020. The CO levels in the atmosphere did not reduce during lockdown; instead, it peaked in March, April, and May 2020, when compared to 2019, possibly due to incomplete combustion of materials containing carbon materials like wood, plastics, etc. This study demonstrates that it is possible to rapidly reduce atmospheric pollution in India. However, since the level of certain pollutants like O3 are dependent on others like NO2, reducing the atmospheric pollution globally is a sustained and concerted effort by all concerned.

A Climate Hyperspectral Infrared Radiance Product (CHIRP) Combining the AIRS and CrIS Satellite Sounding Record

A Climate Hyperspectral Infrared Radiance Product (CHIRP) is introduced combining data from the Atmospheric Infrared Sounder (AIRS) on NASA’s EOS-AQUA platform, the Cross-Track Infrared Sounder (CrIS) sounder on NASA’s SNPP platform, and continuing with CRIS sounders on the NOAA/NASA Joint Polar Satellite Series (JPSS) of polar satellites. The CHIRP product converts the parent instrument’s radiances to a common Spectral Response Function (SRF) and removes inter-satellite biases, providing a consistent inter-satellite radiance record. The CHIRP record starts in September 2002 with AIRS, followed by CrIS SNPP and the JPSS series of CrIS instruments. The CHIRP record should continue until the mid-2040’s as additional JPSS satellites are launched. These sensors, in CHIRP format, provide the climate community with a homogeneous sensor record covering much of the infrared. We give an overview of the conversion of AIRS and CrIS to CHIRP, and define the SRF for common CHIRP format. Considerable attention is paid to removing static bias offsets among these three sensors. The CrIS instrument on NASA’s SNPP satellite is used as the calibration standard. Simultaneous Nadir Overpasses (SNOs) as well as large statistical samplings of radiances from these three satellites are used to derive the instrument bias offsets and estimate the bias offset accuracy, which is ~0.03 K. In addition, possible scene-dependent calibration differences between CHIRP derived from AIRS and CHIRP derived from CrIS on the SNPP platform are presented.