1. Introduction
Hyperspectral infrared/microwave (IR/MW) sounding systems have continuously flown onboard environmental satellites in low-Earth orbit beginning with the launch of the Atmospheric Infrared Sounder (AIRS) on board the U.S. National Aeronautics and Space Administration (NASA) Aqua satellite in 2002 (Chahine et al. 2006). Since the launch of AIRS, the U.S. National Oceanic and Atmospheric Administration (NOAA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) have committed to long-term operational satellite missions featuring IR/MW sounding systems, these being the U.S. Joint Polar Satellite System (JPSS; Goldberg et al. 2013) and the EUMETSAT Polar System. The Suomi National Polar-Orbiting Partnership (SNPP) satellite launched in 2011 features the hyperspectral Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) system. The follow-on JPSS series will feature CrIS/ATMS on board four satellites launched in the same orbit over the next two decades beginning in 2017. These instruments are designed to retrieve atmospheric vertical temperature and moisture profiles under nonprecipitating conditions with the best possible vertical resolution allowed by passive measurements.
The current operational retrieval algorithm for SNPP is the NOAA Unique Combined Atmospheric Processing System (NUCAPS) developed at NOAA.1 The NUCAPS retrieval is based upon the heritage AIRS/Advanced Microwave Sounding Unit (AMSU) system, with the combined IR/MW retrieval algorithm being a modular implementation of the AIRS Science Team algorithm, version 5 (Susskind et al. 2003, 2011). Operational users of the SNPP NUCAPS retrievals include NOAA/National Weather Service (NWS) Weather Forecast Offices [viz., Advanced Weather Interactive Processing System (AWIPS)].
To support intensive calibration/validation (cal/val), JPSS has funded ship-based dedicated radiosonde observations (raobs), including NOAA Aerosols and Ocean Science Expeditions (AEROSE; Morris et al. 2006; Nalli et al. 2011, 2013) in the tropical Atlantic Ocean and the CalWater campaign in the North Pacific Ocean (Ralph et al. 2016). With an eye toward weather forecast and science user applications, we use data from these campaigns to demonstrate the performance of NUCAPS SNPP satellite sounder moisture profile retrievals for observing a variety of hydrometeorological phenomena spanning moisture transport regimes that are theoretically difficult to resolve but located in critical raob-sparse regions over oceans, specifically Saharan air layers (SALs), Hadley cells, and atmospheric rivers (ARs).
Saharan air layers are stable layers of dry, warm air of desert origin that advect across the tropical Atlantic Ocean, often accompanying high levels of Saharan dust aerosols (Carlson and Prospero 1972) and bounded to its south by the intertropical convergence zone (ITCZ; Tsamalis et al. 2013). Previous research has shown that these stabilizing conditions impact hurricane activity over the Atlantic (e.g., Karyampudi and Pierce 2002; Dunion and Velden 2004; Wong and Dessler 2005; Evan et al. 2006). Nalli et al. (2005) assembled trans-Atlantic cross sections of relative humidity (RH) from 3-hourly raobs showing the SAL during a major dust outflow event, and Nalli et al. (2006) showed collocated cross sections from the AIRS. Shu and Wu (2009) later utilized AIRS RH retrievals for studying SAL impact on tropical cyclone intensity (finding both positive and negative influence), but also acknowledged that “uncertainty should be considered [in a future study] in the AIRS/Aqua RH profiles.”
Hadley cells are global-/synoptic-scale circulation cells consisting of thermally driven uplift along the ITCZ axis and associated poleward divergence aloft, until subtropical subsidence eventually causes drying and warming, leading to deep tropospheric columns of extremely dry air with stabilizing inversions at their bases. The resulting tropical RH distribution is of importance in the context of climate change feedbacks (e.g., Dessler and Sherwood 2009), as well as in ascertaining and monitoring the expansion of the tropical belt in response to climate change (e.g., Seidel et al. 2008).
Finally, ARs are narrow channels of moisture transport that are associated with midlatitude storm systems and that can extend thousands of kilometers offshore (e.g., Dettinger et al. 2015; Dacre et al. 2015; Ramos et al. 2016). Understanding ARs is important for forecasting coastal precipitation (e.g., the U.S. West Coast), and as “drought busters” (Dettinger 2013) they have received more attention in light of the record 2012–15 California drought (e.g., Swain 2015). There has been some debate about whether the moisture sources of ARs are nonlocal (i.e., tropical/subtropical moisture export and continuous long-range transport) or local (i.e., moisture conveyor belts associated with midlatitude storms; e.g., Bao et al. 2006; Dacre et al. 2015). However, it appears that there has been recent consensus that they variously incorporate both local and nonlocal sources (e.g., Dettinger et al. 2015; Ramos et al. 2016).
2. Assessing performance within moisture transport regimes
We use data collected during boreal winter (January–February) from the 2013 AEROSE and the 2015 CalWater Atmospheric Radiation Measurement (ARM) Cloud Aerosol Precipitation Experiment (ACAPEX), both on board the NOAA ship Ronald H. Brown. Because SAL and AR phenomena impact the development of landfalling weather systems, focused “spot assessments” of NUCAPS water vapor performance under these conditions is desirable. Satellite cal/val truth datasets acquired during AEROSE and ACAPEX consist of dedicated raobs from Vaisala RS92 radiosondes launched ≃15–60 min prior to satellite overpasses. The AEROSE raob data have undergone Global Climate Observing System (GCOS) Reference Upper-Air Network processing (Bodeker et al. 2016). Because the AEROSE and CalWater/ACAPEX data were intentionally not assimilated into numerical weather prediction (NWP) models, they constitute independent correlative truth data ideally suited for satellite cal/val (Nalli et al. 2011). Collocations of NUCAPS footprints with dedicated raobs are facilitated via the NOAA Products Validation System (Reale et al. 2012).
The ability of the NUCAPS SNPP moisture profile product to detect and resolve coherent moisture structures over oceans is highlighted below through rigorous quantitative statistical analyses of water vapor layer abundance and qualitative cross-sectional analyses of RH. Root-mean-square error (RMSE), bias, and standard deviation σ statistics versus raob2 are calculated on forward model layers (
a. Tropical Atlantic Saharan air layers and Hadley cells
During the 2013 AEROSE, 69 raobs were acquired for SNPP overpasses. Figure 1 shows the launch locations along with the single-closest collocated SNPP footprints (used in the cross-sectional analysis below). A statistical summary of the NUCAPS moisture profile retrievals versus raob (RMSE and bias ±1σ) is given in Fig. 2 for all retrievals within 75 km of launches (results only for cases accepted by the quality flag are shown in Fig. 2, bottom). For reference, results from collocated model output (analysis or forecast nearest in time with satellite overpasses) from the European Centre for Medium-Range Weather Forecasts (ECMWF) are also shown. JPSS requirements for moisture are defined for global performance (RMSE uncertainty for accepted cases over three broad tropospheric layers; cf. Nalli et al. 2013); there are no regional requirements. Although it is not expected that the moisture retrievals would meet the global requirement for these difficult cases on finer forward model layers (
Dedicated Vaisala RS92 radiosonde launch locations (red plus signs) and nearest collocated (accepted or rejected) NUCAPS footprints (blue circles) for the 2013 AEROSE campaign with three transects defined by northwest–southeast (9–23 Jan), north–south (from 23 Jan to 2 Feb), and southeast–northwest (3–13 Feb).
Citation: Journal of Hydrometeorology 17, 12; 10.1175/JHM-D-16-0163.1
Statistical assessment of the NUCAPS satellite sounder moisture profiles vs dedicated raob collocations for retrievals within 
Citation: Journal of Hydrometeorology 17, 12; 10.1175/JHM-D-16-0163.1
A qualitative understanding of the satellite sounder capability for observing hydrometeorological features is given by cross-sectional contour analyses of RH in Fig. 3. We consider the raob cross sections (labeled in the figure) first, where we see a well-defined and persistent marine boundary layer (MBL) with high RH values generally at or below 800 hPa. In the northwest–southeast transect (Fig. 3a), the MBL hovers roughly between 850 and 800 hPa. There is a gradual northwest–southeast drying out of the free troposphere (above 800 hPa) beginning around 70°–65°W, with the exception of a moisture pocket in the mid- to upper troposphere (above 600 hPa) appearing around 38°–30°W that overlies a dry layer from 800 to 500 hPa. Following this, the cruise track turned south. The north–south raob cross section (Fig. 3c) shows extensive tropospheric drying throughout the column, with the MBL becoming increasingly shallower until the ITCZ axis is reached around 4°N. The mid- to upper-tropospheric drying is related to subtropical Hadley subsidence, whereas the lower-level dry layers are SAL phenomena bordered to the south by the ITCZ (e.g., Tsamalis et al. 2013). Although tropospheric moisture associated with the ITCZ is found north of the axis, the MBL remains suppressed with a shallow dry layer sandwiched around 900 hPa. Overall, the collocated NUCAPS moisture cross sections (Figs. 3b,d) correctly capture, in both placement and magnitude, gross features such as the ITCZ, MBL, and large dry regions associated with subtropical Hadley cell subsidence. Shallow SAL filaments are difficult to resolve or detect by a passive sounder, but low-level dryness associated with moderate dust outflow observed beginning around 25 January is clearly seen along with suppressed vertical development of the MBL.
Zonal cross-sectional analyses of RH for 2013 AEROSE (a),(b) northwest–southeast and (c),(d) north–south transects where (a),(c) show the raob RH and (b),(d) show NUCAPS SNPP satellite sounder derived RH from the nearest collocated (accepted or rejected) retrievals. Discontinuities (e.g., 4°N, north–south transect) are due to changes in ship speed and heading, including stations.
Citation: Journal of Hydrometeorology 17, 12; 10.1175/JHM-D-16-0163.1
b. Eastern Pacific Ocean atmospheric rivers
The CalWater campaign was conducted during January–March 2015 as a joint collaborative effort (Ralph et al. 2016). Several aircraft were deployed as well as the Ronald H. Brown, the latter hosting the ACAPEX subcampaign and Second ARM Mobile Facility (AMF2); this work focuses solely on the ACAPEX dataset. There were a total of 66 radiosondes dedicated to SNPP overpasses; Fig. 4 shows the launch locations and collocated SNPP footprints. During both legs of ACAPEX the ship spent a large fraction of time holding station at three locations within the vicinity of ARs. As in section 2a, Fig. 5 provides the statistical summaries of NUCAPS and ECMWF moisture versus raobs (retrievals within 50-km radii) on forward model layers. NUCAPS moisture retrieval statistics for these extremely difficult atmospheric cases are here seen to be reasonably close to ECMWF up to ≃300 hPa, with a dry bias 
As in Fig. 1, but for the 2015 CalWater/ACAPEX campaign, with leg 1 (16–29 Jan) consisting of the Honolulu to San Francisco track and leg 2 (2–11 Feb) consisting primarily of two stations held just outside the U.S. exclusive economic zone at approximately (37°N, 127.25°W) and (34°N, 125°W).
Citation: Journal of Hydrometeorology 17, 12; 10.1175/JHM-D-16-0163.1
As in Fig. 2, but for the 2015 CalWater/ACAPEX campaign.
Citation: Journal of Hydrometeorology 17, 12; 10.1175/JHM-D-16-0163.1
Because the ship remained stationary while holding stations, RH cross sections are plotted as a function of time (instead of space) in Fig. 6. In the cross-sectional analyses the AR phenomena are clearly evident in both the raob and satellite NUCAPS data as ribbons of moisture throughout the troposphere extending up to 200 hPa. This is in stark contrast with the extremely dry tropospheric conditions observed during AEROSE (Fig. 3). Under these conditions, the satellite sounder primarily has difficulty in regions of heavy convection manifested by thick, uniform cloud cover and/or precipitation, which are opaque in the IR spectrum and difficult to cloud-clear sufficiently. An example of this was the missed vertical extent of moisture above 500 hPa on 22 January (Fig. 6b) and below 700 hPa just before 6 February (Fig. 6d); note that both of these cases were rejected by the NUCAPS quality flag. However, in spite of these difficulties, we find that the NUCAPS satellite sounder retrievals still provide offshore AR moisture information in what would otherwise be sounding data voids in near-real-time forecast user applications.
As in Fig. 3, but for the 2015 CalWater/ACAPEX (a),(b) leg 1 and (c),(d) leg 2. Discontinuities are due to changes in ship speed and heading, including stations, and the white areas are from data loss due to radiosonde malfunctions.
Citation: Journal of Hydrometeorology 17, 12; 10.1175/JHM-D-16-0163.1
3. Discussion
This work illustrates satellite sounder moisture profile performance based upon a unique collection of datasets obtained over the Pacific and Atlantic Oceans during boreal winter (January–February) under very different hydrometeorological conditions associated with moisture transport mechanisms (advection and convection) of interest to forecast and science users. In cross-sectional analyses of RH, the NUCAPS SNPP satellite sounder moisture retrievals are shown to be capable of providing information about the distribution of tropospheric water vapor, including mesoscale SALs (associated with desert outflows including low-level stable inversions and aeolian mineral dust) and ARs (associated with large moisture fluxes leading to upstream precipitation over the U.S. West Coast), as well as synoptic-scale Hadley subsidence cells and ITCZ. Statistical analyses of the SNPP NUCAPS retrievals (including rejected cases) versus independent dedicated raobs in these domains were found to be reasonably close to JPSS global performance specifications and ECMWF model output. While ocean cases are often considered “easy” within the satellite sounder community, our cases featured atmospheric conditions that pose difficulties for passive sounder retrievals, including significant inversions associated with the SAL and subsidence, tropical convection within the ITCZ, heavy uniform cloud cover and precipitation associated with ARs, and IR attenuation from Saharan dust aerosols. Although this paper has focused on the SNPP NUCAPS product, we expect similar performance for other satellite hyperspectral sounders and retrieval algorithms (e.g., AIRS).
As a final note regarding SAL and AR phenomena, it is our observation that, although they result from different underlying dynamics, they may be conceptually viewed as inverses of one another in terms of their nonlocal impact due to moisture transport. The latter are defined by narrow water vapor corridors of marine convective origin that advect moisture over the continent downstream, whereas the former are narrow layers of dry, warm air of desert origin that advect stabilizing “negative moisture” over the ocean downstream.
Acknowledgments
This research (N. R. Nalli, C. D. Barnet, T. Reale, Q. Liu, C. Tan, B. Sun, and F. Tilley) was supported by the NOAA/NESDIS Joint Polar Satellite System (JPSS) Office and the Center for Satellite Applications and Research (STAR) Satellite Meteorology and Climatology Division. NCAS (V. Morris and E. Joseph) is funded by NOAA/EPP/MSI Cooperative Agreement NA11SEC4810003. AEROSE works in collaboration with the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) Northeast Extension (PNE) and is supported by NOAA Grants NA17AE1625 (Educational Partnership Program) and NA17AE1623. CalWater 2015/ACAPEX investigators (J. R. Spackman and D. Wolfe) were supported by research funds from the Physical Sciences Division at the NOAA Earth System Research Laboratory. ACAPEX was supported by the U.S. DOE ARM program. GCOS Reference Upper-Air Network (GRUAN) reprocessing was performed courtesy of R. Dirksen (GRUAN Lead Center). L. R. Leung was supported by the U.S. DOE Office of Science Biological and Environmental Research Regional and Global Climate Modeling program (Grant KP17030010). The Pacific Northwest National Laboratory is managed by Battelle for the U.S. DOE under contract DE-AC05-76RLO1830. We acknowledge NUCAPS collaborators for their support of NUCAPS development and validation: A. Gambacorta [Science and Technology Corporation (STC)], F. Iturbide-Sanchez, M. Wilson, K. Zhang, and A. K. Sharma. We are grateful to AEROSE and CalWater/ACAPEX collaborators: C. Fairall and J. Intrieri (chief scientists onboard the Ronald H. Brown); N. Hickmon and M. Ritsche (AMF2 facility managers); M. Oyola and E. Roper [Howard University (HU) NOAA Center for Atmospheric Sciences (NCAS)]; J. W. Smith [National Research Council (NRC)]; M. Szczodrak and M. Izaguirre [University of Miami (UM) Rosenstiel School of Marine and Atmospheric Science (RSMAS)]; and countless students and crews of the NOAA Ronald H. Brown. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or decision.
REFERENCES
Bao, J. W., Michelson S. A. , Neiman P. J. , Ralph F. M. , and Wilczak J. M. , 2006: Interpretation of enhanced integrated water vapor bands associated with extratropical cyclones: Their formation and connection to tropical moisture. Mon. Wea. Rev., 134, 1063–1080, doi:10.1175/MWR3123.1.
Bodeker, G. E., and Coauthors, 2016: Reference upper-air observations for climate: From concept to reality. Bull. Amer. Meteor. Soc., 97, 123–125, doi:10.1175/BAMS-D-14-00072.1.
Carlson, T. N., and Prospero J. M. , 1972: The large-scale movement of Saharan air outbreaks over the northern equatorial Atlantic. J. Appl. Meteor., 11, 283–297, doi:10.1175/1520-0450(1972)011<0283:TLSMOS>2.0.CO;2.
Chahine, M. T., and Coauthors, 2006: AIRS: Improving weather forecasting and providing new data on greenhouse gases. Bull. Amer. Meteor. Soc., 87, 911–926, doi:10.1175/BAMS-87-7-911.
Dacre, H. F., Clark P. A. , Martinez-Alvarado O. , Stringer M. A. , and Lavers D. A. , 2015: How do atmospheric rivers form? Bull. Amer. Meteor. Soc., 96, 1243–1255, doi:10.1175/BAMS-D-14-00031.1.
Dessler, A. E., and Sherwood S. C. , 2009: A matter of humidity. Science, 323, 1020–1021, doi:10.1126/science.1171264.
Dettinger, M. D., 2013: Atmospheric rivers as drought busters on the U.S. West Coast. J. Hydrometeor., 14, 1721–1732, doi:10.1175/JHM-D-13-02.1.
Dettinger, M. D., Ralph F. M. , and Lavers D. , 2015: Setting the stage for a global science of atmospheric rivers. Eos, Trans. Amer. Geophys. Union, 96, doi:10.1029/2015EO038675.
Dunion, J. P., and Velden C. S. , 2004: The impact of the Saharan air layer on Atlantic tropical cyclone activity. Bull. Amer. Meteor. Soc., 85, 353–385, doi:10.1175/BAMS-85-3-353.
Evan, A. T., Dunion J. , Foley J. A. , Heidinger A. K. , and Velden C. S. , 2006: New evidence for a relationship between Atlantic tropical cyclone activity and African dust outbreaks. Geophys. Res. Lett., 33, L19813, doi:10.1029/2006GL026408.
Gambacorta, A., 2013: The NOAA Unique CrIS/ATMS Processing System (NUCAPS). Algorithm Theoretical Basis Doc, version 1.0, NOAA, 73 pp. [Available online at http://www.ospo.noaa.gov/Products/atmosphere/soundings/nucaps/docs/NUCAPS_ATBD_20130821.pdf.]
Gambacorta, A., and Coauthors, 2012: The NOAA Unique CrIS/ATMS Processing System (NUCAPS): First light retrieval results. Proc. ITSC-XVIII, Toulouse, France, International TOVS Working Group, 9 pp. [Available online at https://cimss.ssec.wisc.edu/itwg/itsc/itsc18/program/files/links/1.03_Gambacorta_pa.pdf.]
Goldberg, M. D., Kilcoyne H. , Cikanek H. , and Mehta A. , 2013: Joint Polar Satellite System: The United States next generation civilian polar-orbiting environmental satellite system. J. Geophys. Res. Atmos., 118, 13 463–13 475, doi:10.1002/2013JD020389.
Karyampudi, V. M., and Pierce H. F. , 2002: Synoptic-scale influence of the Saharan air layer on tropical cyclogenesis over the eastern Atlantic. Mon. Wea. Rev., 130, 3100–3128, doi:10.1175/1520-0493(2002)130<3100:SSIOTS>2.0.CO;2.
Miloshevich, L. M., Vömel H. , Whiteman D. N. , and Leblanc T. , 2009: Accuracy assessment and correction of Vaisala RS92 radiosonde water vapor measurements. J. Geophys. Res., 114, D11305, doi:10.1029/2008JD011565.
Morris, V., and Coauthors, 2006: Measuring trans-Atlantic aerosol transport from Africa. Eos, Trans. Amer. Geophys. Union, 87, 565–571, doi:10.1029/2006EO500001.
Nalli, N. R., and Coauthors, 2005: Profile observations of the Saharan air layer during AEROSE 2004. Geophys. Res. Lett., 32, L05815, doi:10.1029/2004GL022028.
Nalli, N. R., and Coauthors, 2006: Ship-based measurements for infrared sensor validation during Aerosol and Ocean Science Expedition 2004. J. Geophys. Res., 111, D09S04, doi:10.1029/2005JD006385.
Nalli, N. R., and Coauthors, 2011: Multi-year observations of the tropical Atlantic atmosphere: Multidisciplinary applications of the NOAA Aerosols and Ocean Science Expeditions (AEROSE). Bull. Amer. Meteor. Soc., 92, 765–789, doi:10.1175/2011BAMS2997.1.
Nalli, N. R., and Coauthors, 2013: Validation of satellite sounder environmental data records: Application to the Cross-track Infrared Microwave Sounder Suite. J. Geophys. Res. Atmos., 118, 13 628–13 643, doi:10.1002/2013JD020436.
Ralph, F. M., and Coauthors, 2016: CalWater field studies designed to quantify the roles of atmospheric rivers and aerosols in modulating U.S. West Coast precipitation in a changing climate. Bull. Amer. Meteor. Soc., 97, 1209–1228, doi:10.1175/BAMS-D-14-00043.1.
Ramos, A. M., Nieto R. , Tomé R. , Gimeno L. , Trigo R. M. , Liberato M. L. R. , and Lavers D. A. , 2016: Atmospheric rivers moisture sources from a Lagrangian perspective. Earth Syst. Dyn., 7, 371–384, doi:10.5194/esd-7-371-2016.
Reale, T., Sun B. , Tilley F. H. , and Pettey M. , 2012: The NOAA Products Validation System. J. Atmos. Oceanic Technol., 29, 629–645, doi:10.1175/JTECH-D-11-00072.1.
Seidel, D. J., Fu Q. , Randel W. J. , and Reichler T. J. , 2008: Widening of the tropical belt in a changing climate. Nat. Geosci., 1, 21–24, doi:10.1038/ngeo.2007.38.
Shu, S., and Wu L. , 2009: Analysis of the influence of Saharan air layer on tropical cyclone intensity using AIRS/Aqua data. Geophys. Res. Lett., 36, L09809, doi:10.1029/2009GL037634.
Susskind, J., Barnet C. D. , and Blaisdell J. M. , 2003: Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds. IEEE Trans. Geosci. Remote Sens., 41, 390–409, doi:10.1109/TGRS.2002.808236.
Susskind, J., Blaisdell J. , Iredell L. , and Keita F. , 2011: Improved temperature sounding and quality control methodology using AIRS/AMSU data: The AIRS Science Team version 5 retrieval algorithm. IEEE Trans. Geosci. Remote Sens., 49, 883–907, doi:10.1109/TGRS.2010.2070508.
Swain, D. L., 2015: A tale of two California droughts: Lessons amidst record warmth and dryness in a region of complex physical and human geography. Geophys. Res. Lett., 42, 9999–10 003, doi:10.1002/2015GL066628.
Tsamalis, C., Chédin A. , Pelon J. , and Capelle V. , 2013: The seasonal vertical distribution of the Saharan air layer and its modulation by the wind. Atmos. Chem. Phys., 13, 11 235–11 257, doi:10.5194/acp-13-11235-2013.
Wong, S., and Dessler A. E. , 2005: Suppression of deep convection over the tropical North Atlantic by the Saharan air layer. Geophys. Res. Lett., 32, L09808, doi:10.1029/2004GL022295.
For more on NUCAPS, the reader is referred to Gambacorta et al. (2012, 2013).
Vaisala reports measurement uncertainty for RS92 radiosondes to be 0.5°C and 5% RH.
