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Results From the plot on the left we can see that the errors are most in the region between 900-1000 hPa. This high values in this region is most likely due to the difficulty in retrieving values near the temperature inversion. Advancing further into the upper atmosphere the Mean Absolute Error (MAE) decreases to less than one. The standard deviation (SDev) approaches the Root-Mean-Square- Error (RMSE) indicating that the dominant error source may be due to randomness. The middle left plot shows a view of the FOR and the ground-track of one RAOB launch. The drift of the balloon adds a level of complexity when comparing RAOB to the NUCAPS profiles since the most representative profile may not be the one closest to the launch site. In this study only the closest grid point to the launch site was used. The middle right plot is a plot of the surface level temperatures and is a demonstration of how the information from the satellite is to visualized. If these values are interpola ted onto an equally spaced grid, a plot similar to the one on the right can be created. This plot shows a vertical cross -section of the at the latitude crossing over Washington D.C. The plots, altogether, show the thermal boundary layer for D.C.. The plot to the right presents a NUCAPS retrieval over NYC. The FOR’s near the city are warmer but a distinct urban heat island signal is not clear. Thermal Boundary Layer Retrievals over the Washington D.C. Metro Area using Satellite-Based NUCAPS-EDRs David Melecio-Vazquez*, J. E. Gonzalez-Cruz*, P. Ramamurthy*, M. Arend*, N. R. Nalli †‡ , Q. Liu † *The City College of New York, New York, NY, † National Oceanic and Atmospheric Administration, ‡ IMSG Inc. Abstract The atmospheric boundary layer (ABL) is made up of layers defined by a characteristic scale that is proportional to the distance from the surface. The smallest scales are found near the surface and the larger scales are found in the upper layers. Most techniques of observing the ABL are based upon upward pointing remote sensors or radiosondes which provide information at a single locale. Satellite data, on the other hand, can provide important domain-wide context for such measurements. Using the NOAA Unique Combined Atmospheric Processing System (NUCAPS), we examine profiles of the temperature and humidity profiles of the ABL for a wide area. In order to evaluate the sounding results from NUCAPS, 28 radiosonde launches were compared against the closest grid point to the Howard University launch site in Beltsville, Maryland. Each FOR is approximately 50 km in diameter. The statistics were calculated for the virtual potential temperature bias and standard deviation between NUCAPS and radisondes (NUCAPS – RAOB). Error statistics in the boundary layer, show the largest errors occur the region between 900-1000 hPa. The high errors in this region is most likely due to the difficulty in retrieving values near the inversion. Advancing further into the upper atmosphere the mean-absolute-error (MAE) decreases to less than one, and the standard deviation approaches the root-mean-square-error indicating that the dominant error source may be due to randomness, in the upper boundary layers, whereas the greater of value of the MAE indicates that lower boundary layer retrievals are more prone to systematic errors. The possibility of identifying urban heat islands is explored using NUCAPS-EDR using retrievals over Washington D.C. and New York City. Introduction The figure shown here (from Fernando, 2010) shows a diagram of the interaction of the wind and cities in the boundary layer. The thermal boundary layer, however, over cities is known to a lesser extent (Barlow 2014) than the momentum and thus the focus of this research, is on temperature and humidity profiles. There is currently a need in the NWP community for this type of data in order to better evaluate NWP model performance (Backlanov et al. 2011). The virtual potential temperature, is calculated using the information of temperature, and water vapor mixing ratio, . is a common variable used in boundary layer studies for observing atmospheric stability. Typical profiles of over homogeneous terrain are shown on the right. The profile on the left shows what is expected for nighttime conditions. A stable layer forms over cool surface and the increases with height, after an unstable region that has residual heat from the previous insolation period. During the day, the boundary layer is expected to have a profile similar to the right profile. The ground is hotter than the air above and it creates a thick unstable layer which is usually called the Mixed Layer. The arrows indicate parcel movement due to buoyancy, and indicates an unstable layer is present. Observations Surface-based remote sensing of the ABL is limited to a single point, and so in this investigation the use of satellite data will help provide coverage over a wide area. The Suomi-NPP satellite was launched in 2011 and the onboard instruments CrIS (Cross-track Infrared Sounder) and ATMS (Advanced Technology Microwave Sounder) provide the profile information. The retrieval of atmospheric temperatures and humidity is performed by the NOAA Unique Combined Atmospheric Profiling System (NUCAPS). NUCAPS profiles are provided at 100 pressure levels. Assuming an exponentially decaying pressure, the boundary layer will lie between the layers 1013 and 700 mb (~3000m high). In the NUCAPS profiles, this corresponds to the first 13 levels up from the surface. However the profiles are limited in vertical resolution and horizontal resolution, as will be discussed. Future Work: Applications for NWP Models Current Numerical Weather Prediction models (NWP) try to replicate the complex urban morphology and anthropogenic heating using simplified representations (Martilli, 2015). A lot of work has been done on modeling the fluxes, (for example: Salamanca et al. 2011 and Gutierrez et al 2015). and the work compiled can be used for testing the resulting profiles of those modeled fluxes over urban terrain. Future work will focus on bridging the gap between the observations of the temperature profile in urban areas with the results given by the current urbanized version of the Weather Research and Forecast Model. On the bottom left is a climatological average References • Baklanov AA, Grisogono B, Bornstein R, et al (2011) The Nature, Theory, and Modeling of Atmospheric Planetary Boundary Layers. Bull Am Meteorol Soc 92:123–128. doi: 10.1175/2010BAMS2797.1 • Cimini D, Visconti G, Marzano FS (eds) (2011) Integrated Ground-Based Observing Systems. Springer Berlin Heidelberg, Berlin, Heidelberg • Fernando HJS (2010) Fluid Dynamics of Urban Atmospheres in Complex Terrain. Annu Rev Fluid Mech 42:365–389. doi: 10.1146/annurev-fluid-121108-145459 • Gutiérrez E, González JE, Martilli A, et al (2015) Simulations of a Heat-Wave Event in New York City Using a Multilayer Urban Parameterization. J Appl Meteorol Climatol 54:283–301. doi: 10.1175/JAMC-D-14-0028.1 • Martilli A, Santiago JL, Salamanca F (2015) On the representation of urban heterogeneities in mesoscale models. Environ Fluid Mech 15:305–328. doi: 10.1007/s10652-013-9321-4 • Salamanca F, Martilli A, Tewari M, Chen F (2011) A Study of the Urban Boundary Layer Using Different Urban Parameterizations and High-Resolution Urban Canopy Parameters with WRF. J Appl Meteorol Climatol 50:1107–1128. doi: 10.1175/2010JAMC2538.1 ATMS CrIS S-NPP Image Credits Suomi-NPP Image: https://eyes.nasa.gov/ , ATMS: http://www.jpss.noaa.gov/atms.html , CrIS: http://www.jpss.noaa.gov/cris.html Acknowledgements • The National Oceanic and Atmospheric Administration – Cooperative Remote Sensing Science and Technology Center (NOAA-CREST). NOAA CREST - Cooperative Agreement No: NA11SEC4810004. • The IMSG-CUNY Student Training Program was supported by JPSS. • The Howard University Beltsville site is supported by the NOAA Center for Atmospheric Sciences (NCAS) and JPSS. • Brookhaven National Laboratory for their support in my research. 97 th American Meteorological Society Annual Meeting, January 21-26, 2017, Seattle, WA, USA Metrics and Methods The NUCAPS algorithm calculates vertical profiles of temperature at 100 pressure levels and mixing ratio at 100 “effective” pressure levels. For the following calculations the mixing ratios were interpolated linearly to the temperature pressure levels and the was calculated at these levels. The profile is extracted from information from the CrIS and ATMS instrument in a Field of Regard (FOR) of 50km in diameter. In order to evaluate the sounding results from NUCAPS, 28 radiosonde launches were compared against the closest grid point profile. Error statistics were calculated on the bias of the NUCAPS and radiosonde profiles (denoted RAOB). A total of three error metrics were used: MAE, RMSE, and the standard deviation (SDev). The equations are given below. : , = 1 + 0.61 , ; = 0 0.286 : , = , − , : = 1 =1 , : = =1 , 2 is known as the potential temperature, is the water vapor mixing ratio, 0 is a reference pressure which was set to 1013.948 mb, is the local pressure which comes from the pressure levels in the retrieval.
