Surface Pressure Observations Research Articles

Sea surface barometry with an O2 differential absorption radar: retrieval algorithm development and simulation

Sea surface air pressure observations are a significant gap in the current Earth observing systems. This study addresses retrieval algorithm development and the evaluation of the potential impact of instrumental and environmental uncertainties on sea level pressure retrievals for the measurements of O2 differential absorption radar systems operating at three spectrally evenly spaced close-frequency bands (65.5, 67.75, and 70.0 GHz). A simulated northern hemispheric summer case is used to simulate retrieval uncertainties. To avoid high attenuation and a low signal-to-noise ratio, radar measurements from weather conditions with a rain rate ≥1 mm/h are not used in the retrieval. This study finds that a retrieval algorithm combining all three channels, i.e., the 3-channel approach, can effectively mitigate major atmospheric and sea surface influences on sea surface air pressure retrieval. The major uncertainty of sea surface pressure retrieval is due to the standard deviation in radar power returns. Analysis and simulation demonstrate the potential of global sea surface pressure observations with errors of about 1∼2 mb, which is urgently needed for the improvement of numerical weather prediction models. Future work will emphasize instrument development and field experiments. It is anticipated that an O2 differential absorption radar system will be available for meteorological applications in a few years.

Assimilation of surface pressure observations from personal weather stations in AROME-France

Abstract. Spatially dense surface pressure observations from personal weather stations (PWSs) are able to describe pressure patterns at the surface, such as those associated with convective events, in more detail than with standard weather stations (SWSs) only. In this study, the benefit of assimilating PWS observations with the 3DVar and the 3DEnVar data assimilation schemes of the AROME-France model is evaluated over a 1-month period and during a heavy precipitation event in the South of France. Observations of surface pressure from PWSs are bias-corrected, quality-controlled, and thinned with a spacing equal to the horizontal dimension of an AROME-France grid cell. Over France, almost half of the 55 187 available PWS observations are assimilated, which is 129 times more than the number of assimilated SWS observations. Despite the limited advantages found from their assimilation with the 3DVar assimilation scheme, the 3DEnVar assimilation scheme shows systematic improvement and reduces by 10.3 % the root-mean-square deviation in surface pressure between 1 h model forecasts and SWS observations over France. Significant improvement is observed over the first 9 h of the forecasts in mean sea level pressure. Finally, when PWS observations are assimilated with the 3DEnVar assimilation scheme, a surface pressure anomaly generated by a mesoscale convective system – observed by PWSs and not visible without them – is successfully assimilated. In that case, the forecasts of location and temporal evolution of the mesoscale convective system as well as rainfall are closer to the observations when PWS observations are assimilated.

Perseverance MEDA Atmospheric Pressure Observations—Initial Results

AbstractThe Mars2020 Perseverance Rover landed successfully on the Martian surface on the Jezero Crater floor (18.44°N, 77.45°E) at Martian solar longitude, Ls, ∼5° in February 2021. Since then, it has produced highly valuable environmental measurements with a versatile scientific payload including the MEDA (Mars Environmental Dynamics Analyzer) suite of environmental sensors. One of the MEDA systems is the PS pressure sensor system, which weighs 40 g and has an estimated absolute accuracy of better than 3.5 Pa and a resolution of 0.13 Pa. We present initial results from the first 414 sols of Martian atmospheric surface pressure observations by the PS, whose performance was found to meet its specifications. Observed sol‐averaged atmospheric pressures follow an anticipated pattern of pressure variation in the course of the advancing season and are consistent with data from other landing missions. The observed daily pressure amplitude varies by ∼2%–5 % of the sol‐averaged pressure, with absolute amplitude 10–35 Pa in an approximately direct relationship with airborne dust. During a regional dust storm, which began at Ls ∼ 135°, the daily pressure amplitude roughly doubled. The daily pressure variations were found to be remarkably sensitive to the seasonal evolution of the atmosphere. In particular, analysis of the daily pressure signature revealed diagnostic information likely related to the regional scale structure of the atmosphere. Comparison of Perseverance pressure observations with data from other landers reveals the global scale seasonal behavior of Mars' atmosphere.

Global Historical Reanalysis with a 60-km AGCM and Surface Pressure Observations: OCADA

Global Historical Reanalysis with a 60-km AGCM and Surface Pressure Observations: OCADA

Ensemble Forecast Sensitivity to Observations Impact (EFSOI) applied to a regional data assimilation system over south-eastern South America

Observations that are assimilated into numerical weather prediction systems are conformed by numerous data sets and their impact should be objectively evaluated. This can be efficiently achieved by the Forecast Sensitivity to Observation Impact (FSOI) methodology. In this study we explore the application of the ensemble formulation of FSOI (EFSOI) in a regional data assimilation system over south-eastern South America and evaluate the observations that result beneficial or detrimental to the forecast. In this paper we focus on the impact of two types of surface weather stations: human-operated conventional surface weather station networks from the National Meteorological Service and non-conventional automatic surface weather stations from different public and private networks (CSWS and NSWS respectively). To achieve this, the Weather Research and Forecasting model coupled with the Local Ensemble Transform Kalman Filter is used with 20 members. The experiment was carried out during 30 days of the intensive observing period of the RELAMPAGO-CACTI (Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations-Clouds, Aerosols, and Complex Terrain Interaction) field campaign that was conducted during the 2018–2019 austral warm season in the center of Argentina. 20 km resolution analyses were obtained every 6-h, assimilating data from soundings, aircrafts, GOES derived motion winds, AIRS retrievals, CSWS and NSWS. It is shown that, considering the entire period, all the observation sources had a positive impact on the 6-h forecasts. However, when each variable is analyzed individually, surface pressure observations from both CSWS and NSWS show on average a negative impact. Two data denial experiments were carried out in order to support EFSOI results. This paper is the first approximation to quantify the impact of different observation sources and observed variables at a regional scale over south-eastern South America, a data sparse region where unconventional networks can help close large geographical data gaps. The results of this work help identify observation data sources detrimental for the data assimilation system, suggesting data selection criteria to improve the regional forecast accuracy.

Impacts of Marine Surface Pressure Observations from a Spaceborne Differential Absorption Radar Investigated with an Observing System Simulation Experiment

Abstract A new instrument has been proposed for measuring surface air pressure over the marine surface with a combined active/passive scanning multichannel differential absorption radar to provide an estimate of the total atmospheric column oxygen content. A demonstrator instrument, the Microwave Barometric Radar and Sounder (MBARS), has been funded by the National Aeronautics and Space Administration for airborne test missions. Here, a proof-of-concept study to evaluate the potential impact of spaceborne surface pressure data on numerical weather prediction is performed using the Goddard Modeling and Assimilation Office global observing system simulation experiment (OSSE) framework. This OSSE framework employs the Goddard Earth Observing System model and the hybrid 4D ensemble variational Gridpoint Statistical Interpolation data assimilation system. Multiple flight and scanning configurations of potential spaceborne orbits are examined. Swath width and observation spacing for the surface pressure data are varied to explore a range of sampling strategies. For wider swaths, the addition of surface pressures reduces the root-mean-square surface pressure analysis error by as much as 20% over some ocean regions. The forecast sensitivity observation impact tool estimates impacts on the Pacific Ocean basin boundary layer 24-h forecast temperatures for spaceborne surface pressures that are on par with rawinsondes and aircraft and estimates greater impacts than the current network of ships and buoys. The largest forecast impacts are found in the Southern Hemisphere extratropics.

Impact of GPS radio occultation assimilation on the 18–21 July 2021 heavy rainfall event in Henan

The Gridpoint Statistical Interpolation (GSI) assimilation system and Weather Research & Forecasting (WRF) mesoscale regional model were used to investigate the sensitivity of assimilating Global Positioning System radio occultation (GPS-RO) data for a “21·7” heavy rainfall event in Henan Province. The Ensemble Forecast Sensitivity to Observation (EFSO) algorithm was used to assess the influence of the GPS-RO assimilation relative to conventional heavy rainfall observations in Henan. Sensitivity experiments revealed that the 6-h accumulated precipitation simulated by the three experiments surpassed 200 mm, which was similar to the automated station observations, and the rainfall center location and intensity simulated by the assimilated GPS-RO experiment (EXPR2) were also similar to the observations. Following GPS-RO data assimilation, the upper-level west border continental high pressure was improved, which increased the pressure gradient on the east and west sides of the heavy rainfall, resulting in enhanced dynamic lifting and water vapor flow from the lower level to the whole Henan downpour center. The entire convective convergence rising height increased, which had an optimum influence on the dynamic and thermal conditions of heavy rainfall. The EFSO estimates revealed that most observations improved the model predictions, with the conventional surface pressure and U-wind observations making a positive standardized contribution of approximately 0.05 and 0.075, respectively, but T(Temperature) and Q(Specific humidity) making a negative standardized contribution of approximately −0.025. The GNSS GPS-RO greatly improved the prediction results by decreasing the error by 0.125, and the National Satellite Meteorological Center (NSMC) FY-3D contributed the most to the model.

Impacts of Northeastern Pacific Buoy Surface Pressure Observations

Abstract Under the Atmospheric River Reconnaissance (AR Recon) Program, ocean drifting buoys (drifters) that provide surface pressure observations were deployed in the northeastern Pacific Ocean to improve forecasts of U.S. West Coast high-impact weather. We examine the impacts of both AR Recon and non-AR Recon drifter observations in the U.S. Navy’s global atmospheric data assimilation (DA) and forecast system using data-denial experiments and forecast sensitivity observation impact (FSOI) analysis, which estimates the impact of each observation on the 24-h global forecast error total energy. Considering all drifters in the eastern North Pacific for the 2020 AR Recon season, FSOI indicates that most of the beneficial impacts come from observations in the lowest quartile of observed surface pressure values, particularly those taken late in the DA window. Observations in the upper quartile have near-neutral impacts on average and are slightly nonbeneficial when taken late in the DA window. This may occur because the DA configuration used here does not account for model biases, and innovation statistics show that the forecast model has a low pressure bias at high pressures. Case studies and other analyses indicate large beneficial impacts coming from observations in regions with large surface pressure gradients and integrated vapor transport, such as fronts and ARs. Data-denial experiments indicate that the assimilation of AR Recon drifter observations results in a better-constrained analysis at nearby non-AR Recon drifter locations and counteracts the NAVGEM pressure bias. Assimilating the AR Recon drifter observations improves 72- and 96-h Northern Hemisphere forecasts of winds in the lower and middle troposphere, and geopotential height in the lower, middle, and upper troposphere. Significance Statement The purpose of this study is to understand how observations of atmospheric pressure at the ocean surface provided by drifting buoys impact weather forecasts. Some of these drifting buoys were deployed under a program to study atmospheric rivers (ARs) to improve forecasts of high-impact weather on the West Coast. We find that these observations are most effective at reducing forecast errors when taken in regions near fronts and cyclones. The additional drifting buoys deployed under the AR Reconnaissance project reduce forecast errors at 72 and 96 h over North America and the Northern Hemisphere. These results are important because they illustrate the potential for improving forecasts by increasing the number of drifting buoy surface pressure observations over the world oceans.

Assessing homogeneity of land surface air temperature observations using sparse‐input reanalyses

AbstractState‐of‐the‐art homogenisation approaches for any test site rely upon the availability of a sufficient number of neighbouring sites with similar climatic conditions and a sufficient quantity of overlapping measurements. These conditions are not always met, particularly in poorly sampled regions and epochs. Modern sparse‐input reanalysis products which are constrained by observed sea surface temperatures, sea‐ice and surface pressure observations, continue to improve, offering independently produced surface temperature estimates back to the early 19th century. This study undertakes an exploratory analysis on the applicability of sparse‐input reanalysis to identify breakpoints in available basic station data. Adjustments are then applied using a variety of reanalysis and neighbour‐based approaches to produce four distinct estimates. The methodological independence of the approach may offer valuable insights into historical data quality issues. The resulting estimates are compared to Global Historical Climatology Network version 4 (GHCNMv4) at various aggregations. Comparisons are also made with five existing global land surface monthly time series. We find a lower rate of long‐term warming which principally arises in differences in estimated behaviour prior to the early 20th century. Differences depend upon the exact pair of estimates, varying between 15 and 40% for changes from 1850–1900 to 2005–2014. Differences are much smaller for metrics starting after 1900 and negligible after 1950. Initial efforts at quantifying parametric uncertainty suggest this would be substantial and may lead to overlap between these new estimates and existing estimates. Further work would be required to use these data products in an operational context. This would include better understanding the reasons for apparent early period divergence including the impact of spatial infilling choices, quantification of parametric uncertainty, and a means to update the product post‐2015 when the NOAA‐CIRES‐DOE 20CRv3 sparse input reanalysis product, upon which they are based, presently ceases.

Visualizing Lamb Waves From a Volcanic Eruption Using Meteorological Satellite Himawari‐8

AbstractThe major eruption of volcano Hunga Tonga‐Hunga Ha'apai in Tonga on 15 January 2022 generated atmospheric Lamb waves that propagated over the world. This seemed the first volcanic Lamb wave event that was clearly captured by the state‐of‐the‐art geostationary meteorological satellites. This paper provides visualization of the waves using thermal infrared images from geostationary satellite Himawari‐8. The second time derivatives of 10‐min interval images clearly illustrated the waves propagating at about 310 m s−1. The waves could be tracked for more than a week while they propagated five times round the earth. Automated wavefront identification illustrated nonuniform propagation speed. The signals in the satellite images well matched the surface pressure observations in Japan.

On the Development of GFDL's Decadal Prediction System: Initialization Approaches and Retrospective Forecast Assessment

AbstractUsing GFDL's new coupled model SPEAR, we have developed a decadal coupled reanalysis/initialization system (DCIS) that does not use subsurface ocean observations. In DCIS, the winds and temperature in the atmosphere, along with sea surface temperature (SST), are restored to observations. Under this approach the ocean component of the coupled model experiences a sequence of surface heat and momentum fluxes that are similar to observations. DCIS offers two initialization approaches, called A1 and A2, which differ only in the atmospheric forcing from observations. In A1, the atmospheric winds/temperature are restored toward the JRA reanalysis; in A2, surface pressure observations are assimilated in the model. Two sets of coupled reanalyses have been completed during 1961–2019 using A1 and A2, and they show very similar multi‐decadal variations of the Atlantic Meridional Overturning Circulation (AMOC). Two sets of retrospective decadal forecasts were then conducted using initial conditions from the A1 and A2 reanalyses. In comparison with previous prediction system CM2.1, SPEAR‐A1/A2 shows comparable skill of predicting the North Atlantic subpolar gyre SST, which is highly correlated with initial values of AMOC at all lead years. SPEAR‐A1 significantly outperforms CM2.1 in predicting multi‐decadal SST trends in the Southern Ocean (SO). Both A1 and A2 have skillful prediction of Sahel precipitation and the associated ITCZ shift. The prediction skill of SST is generally lower in A2 than A1 especially over SO presumably due to the sparse surface pressure observations.

An Evaluation of the Performance of the Twentieth Century Reanalysis Version 3

AbstractThe performance of a new historical reanalysis, the NOAA–CIRES–DOE Twentieth Century Reanalysis version 3 (20CRv3), is evaluated via comparisons with other reanalyses and independent observations. This dataset provides global, 3-hourly estimates of the atmosphere from 1806 to 2015 by assimilating only surface pressure observations and prescribing sea surface temperature, sea ice concentration, and radiative forcings. Comparisons with independent observations, other reanalyses, and satellite products suggest that 20CRv3 can reliably produce atmospheric estimates on scales ranging from weather events to long-term climatic trends. Not only does 20CRv3 recreate a “best estimate” of the weather, including extreme events, it also provides an estimate of its confidence through the use of an ensemble. Surface pressure statistics suggest that these confidence estimates are reliable. Comparisons with independent upper-air observations in the Northern Hemisphere demonstrate that 20CRv3 has skill throughout the twentieth century. Upper-air fields from 20CRv3 in the late twentieth century and early twenty-first century correlate well with full-input reanalyses, and the correlation is predicted by the confidence fields from 20CRv3. The skill of analyzed 500-hPa geopotential heights from 20CRv3 for 1979–2015 is comparable to that of modern operational 3–4-day forecasts. Finally, 20CRv3 performs well on climate time scales. Long time series and multidecadal averages of mass, circulation, and precipitation fields agree well with modern reanalyses and station- and satellite-based products. 20CRv3 is also able to capture trends in tropospheric-layer temperatures that correlate well with independent products in the twentieth century, placing recent trends in a longer historical context.

Study on the Variation Trend of Potential Evapotranspiration in the Three-River Headwaters Region in China Over the Past 20 years

To study the variation trend of potential evapotranspiration (PET) in the Three-River Headwaters (TRH) region of the Qinghai-Tibet Plateau in China, we use 2-m temperature and surface pressure observation data from 14 meteorological weather stations in the TRH region, and the surface PET is calculated by the Penman-Monteith formula. The global land surface data assimilation system (GLDAS) from 2000 to 2018 were used to compare and verify the accuracy and applicability of the calculated PET in the THR region. The results show that in the past 20 years, the PET of 14 weather stations in the TRH region has shown an increasing trend, with annual averaged growth rate of 1.4±1.2 mm·a-1, and the spatial distribution of the annual variation rate of PET has obvious difference. PET is higher in the eastern area of TRH region, and lower in the western area. The drought in this area increased from southeast to northwest, which was consistent with the spatial distribution of precipitation. The aridity index K has fluctuated and increased begore 2015, but there was a sudden change in 2018, and the aridity index K in the TRH began to decline after 2018, and the climate changed from dry to wet.

Simulation of Propagation of Acoustic-Gravity Waves Generated by Tropospheric Front Instabilities into the Upper Atmosphere

A numerical model of the propagation of acoustic-gravity waves excited by pressure fluctuations on the Earth’s surface is developed. Propagation of acoustic-gravity waves generated by instabilities of tropospheric fronts into the upper atmosphere is simulated. The experimental data on atmospheric pressure variations during 2016 year registered on a net of four microbarographs located in the Moscow region are processed. A case of very significant pressure fluctuations (up to 30 times larger than the average level) is selected, which were caused by an atmospheric front arrival. Observed surface pressure field variations for this field were approximated and used as the lower boundary condition for simulating the vertical wave propagation. The numerical simulations showed that just after the boundary source activation, the infrasonic waves in the upper atmosphere may have amplitudes of perturbations of temperature up to 100 K, and horizontal velocity up to 60 m/s. Internal gravity waves come into the upper atmosphere later and far horizontally away from the wave source. The influence of the limited dimensions of the computational domain on the simulation results is investigated. The conditions at the horizontal boundaries of the computational domain, which allow the runaway of waves beyond the domain are proposed. The frequency spectrum of waves in the non-isothermal atmosphere is analyzed.

A Quality Assessment of the Real-Time Mesoscale Analysis (RTMA) for Aviation

Abstract Missing observations at airports can cause delays in commercial and general aviation in the United States owing to Federal Aviation Administration (FAA) safety regulations. The Environmental Modeling Center (EMC) has provided interpolated temperature data from the National Oceanic and Atmospheric Administration’s Real-Time Mesoscale Analysis (RTMA) at airport locations throughout the United States since 2015, with these data substituting for missing temperature observations and mitigating impacts on air travel. A quality assessment of the RTMA is performed to determine if the RTMA could be used in a similar fashion for other weather observations, such as 10-m wind, ceiling, and visibility. Retrospective, data-denial experiments are used to perform the quality assessment by withholding observations from FAA-specified airports. Outliers seen in the RTMA ceiling and visibility analyses during events meeting or exceeding instrument flight rules suggest the RTMA should not be substituted for missing ceiling and visibility observations at this time. The RTMA is a suitable replacement for missing temperature observations for a subset of airports throughout most of the CONUS and Alaska, but not at all stations. Likewise, the RTMA is a suitable substitute for missing surface pressure observations at a subset of airports, with notable exceptions in regions of complex terrain. The RTMA may also be a suitable substitute for missing wind speed observations, provided the wind speed is ≤15 kt (1 kt ≈ 0.51 m s−1). Overall, these results suggest the potential for RTMA to substitute for additional missing observations while highlighting priority areas of future work for improving the RTMA.

Curiosity observations and column model integrations for a martian global dust event

The Curiosity observations of surface pressure, optical thickness (τ), and Mars Year 32 water column values (PWC) were used to force adsorptive column model simulations around the MY34 strong dust event. Hourly ground (Tg) and air temperatures and water vapor mixing ratios were used for model validation. The diurnal amplitude of Tg drops from 90 K to 30 K during thick dust (τ up to 7–8.5), then increases back to 90 K in the pace of τ decreasing back to seasonal average values. The model mimics the very large and rapid temperature changes quite well, strong dust downward IR emission being the dominant term in the surface heat budget during thick dust. Sensitivity tests indicate that the thick dust tends to show more solar forward scatter initially, consistent with larger particles. Water vapor mixing ratios are lower than normal before and after the event but a lot higher during it. The required local PWC at Gale is 2–3 μm lower than during the low-dust MY32 before and after the dust event, but about 7–8 μm higher during the peak of it.

Surface pressure and elevation correction from observation and multiple reanalyses over the Tibetan Plateau

Surface pressure reflects the deep structure of the overlying atmosphere, and is recognized as an indicator of climate change. In this study, observed surface pressure at 71 stations over the Tibetan Plateau (TP) during 1979–2013 is analyzed and compared with monthly means from multiple reanalyses (NCEP1, NCEP2, ERA-Interim, MERRA and JRA55). During the studied period, surface pressure from both observations and the reanalyses increases slowly up until the mid-2000s but shows a decrease afterwards, leading to a recent fall in pressure. However, the surface pressure over the TP in spring has increased, probably explained by the thermal condition such as diabatic heating change. Observations and the multiple reanalyses are positively correlated at most locations indicating that reanalyses reproduce the interannual variation and long-term trend of observed surface pressure fairly well. Despite high inter-annual correlation, trend magnitudes over 1979–2013 are varied, with observations showing decreased pressure at most stations, but reanalyses showing increases in many cases. Compared with observations however, surface pressures from all reanalyses are underestimated usually by about 3–6%. There are significant positive correlations between surface pressure bias and elevation bias, suggesting that overestimation of elevation partially explains the surface pressure bias. A topographical correction method using the hydrostatic equation is therefore conducted and more than 90% of the biases of the reanalyses can be eliminated. Overall, this study points to the importance of better analyzing the importance of topography in the western TP to enhance understanding of reanalysis uncertainties in this region.

BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia

Abstract. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) is the first atmospheric regional reanalysis over a large region covering Australia, New Zealand, and Southeast Asia. The production of the reanalysis with approximately 12 km horizontal resolution – BARRA-R – is well underway with completion expected in 2019. This paper describes the numerical weather forecast model, the data assimilation methods, the forcing and observational data used to produce BARRA-R, and analyses results from the 2003–2016 reanalysis. BARRA-R provides a realistic depiction of the meteorology at and near the surface over land as diagnosed by temperature, wind speed, surface pressure, and precipitation. Comparing against the global reanalyses ERA-Interim and MERRA-2, BARRA-R scores lower root mean square errors when evaluated against (point-scale) 2 m temperature, 10 m wind speed, and surface pressure observations. It also shows reduced biases in daily 2 m temperature maximum and minimum at 5 km resolution and a higher frequency of very heavy precipitation days at 5 and 25 km resolution when compared to gridded satellite and gauge analyses. Some issues with BARRA-R are also identified: biases in 10 m wind, lower precipitation than observed over the tropical oceans, and higher precipitation over regions with higher elevations in south Asia and New Zealand. Some of these issues could be improved through dynamical downscaling of BARRA-R fields using convective-scale (<2 km) models.

Global Observations of Fine-Scale Ocean Surface Topography With the Surface Water and Ocean Topography (SWOT) Mission

The future international Surface Water and Ocean Topography (SWOT) Mission, planned for launch in 2021, will make high-resolution 2D observations of sea-surface height using SAR radar interferometric techniques. SWOT will map the global and coastal oceans up to 77.6° latitude every 21 days over a swath of 120 km (20 km nadir gap). Today’s 2D mapped altimeter data can resolve ocean scales of 150 km wavelength whereas the SWOT measurement will extend our 2D observations down to 15-30 km, depending on sea state. SWOT will offer new opportunities to observe the oceanic dynamic processes at these scales, that are important in the generation and dissipation of kinetic energy in the ocean, and act as one of the main gateways connecting the interior of the ocean to the upper layer. The active vertical exchanges linked to these scales have impacts on the local and global budgets of heat and carbon, and on nutrients for biogeochemical cycles. This review paper highlights the issues being addressed by the SWOT science community to understand SWOT’s very precise SSH / surface pressure observations, and it explores how SWOT data will be combined with other satellite and in-situ data and models to better understand the upper ocean 4D circulation (x,y,z,t) over the next decade. SWOT’s new SAR-interferometry technology aims to observe ocean SSH scales down to 15-30 km in wavelength. At these scales, SSH includes “balanced” geostrophic eddy motions and high-frequency internal tides and internal waves. This presents both a challenge in reconstructing the 4D upper ocean circulation, or in the assimilation of SSH in models, but also an opportunity to have global observations of the 2D structure of these phenomena, and to learn more about their interactions. At these small scales, the ocean dynamics evolve rapidly, and combining SWOT 2D SSH data with other satellite or in-situ data with different space-time coverage is also a challenge. SWOT’s new technology will be a forerunner for the future altimetric observing system, and so advancing on these issues today will pave the way for our future.

Evaluating the Experimental High-Resolution Rapid Refresh–Alaska Modeling System Using USArray Pressure Observations

Abstract The High-Resolution Rapid Refresh–Alaska (HRRR-AK) modeling system provides 3-km horizontal resolution and 0–36-h forecast guidance for weather conditions over Alaska. This study evaluated the experimental version of the HRRR-AK system available from December 2016 to June 2017, prior to its operational deployment by the National Centers for Environmental Prediction in July 2018. Surface pressure observations from 158 National Weather Service (NWS) stations assimilated during the model’s production cycle and pressure observations from 101 USArray Transportable Array (TA) stations that were not assimilated were used to evaluate 265 complete 0–36-h forecasts of the altimeter setting (surface pressure reduced to sea level). The TA network is the largest recent expansion of Alaskan weather observations and provides an independent evaluation of the model’s performance during this period. Throughout the study period, systematic differences in altimeter setting between the HRRR-AK 0-h forecasts were larger relative to the unassimilated TA observations than relative to the assimilated NWS observations. Upon removal of these initial biases from each of the subsequent 1–36-h altimeter setting forecasts, the model’s 36-h forecast root-mean-square errors at the NWS and TA locations were comparable. The model’s treatment of rapid warming and downslope winds that developed in the lee of the Alaska Range during 12–15 February is examined. The HRRR-AK 0-h forecasts were used to diagnose the synoptic and mesoscale conditions during this period. The model forecasts underestimated the abrupt increases in the temperature and intensity of the downslope winds with smaller errors as the downslope wind events evolved.