Cooperative Institute for Research in the Atmosphere

About: Cooperative Institute for Research in the Atmosphere is a based out in . It is known for research contribution in the topics: Snow & Data assimilation. The organization has 332 authors who have published 997 publications receiving 38835 citations. The organization is also known as: CIRA.

Evaluation of NCAR's AutoNowCaster for Operational Application within the National Weather Service

Abstract: NCAR’s AutoNowCaster (ANC) was modified to run over a large domain that encompasses the air traffic management hubs of Chicago, Illinois; New York City, New York; and Atlanta, Georgia. ANC produces nowcasts of convective likelihood (CL), with higher values delineating areas where storms are likely to form and be sustained, and vice versa. This paper presents the results of verifying ANC’s 60-min nowcasts of CL over the study area using data collected from 11 June to 30 September 2012. To reduce the high sensitivity of statistical scores to small errors in location and timing, spatial and temporal relaxation techniques were explored. The results show that, at a spatial scale of roughly 50 km and with no temporal relaxation, a CL value of 0.6 is an optimum threshold for nowcasting the general areas both where new storms may initiate and where existing storms will be sustained. Moreover, at that same spatial scale and with temporal relaxation (45–90 min from the nowcast issuance time), a CL value of ...

Total sulfur analysis of fine particulate mass on nylon filters by ICP–OES

Abstract: Sulfur (S) and sulfate (SO42- ) in fine particulate matter (PM2.5 ) are monitored by the Interagency Monitoring of Protected Visual Environments (IMPROVE) network at remote and rural sites across the United States. Within the IMPROVE network, S is determined from X-ray fluorescence (XRF) spectroscopy from a Teflon filter, and SO42- is determined via ion chromatography (IC) from a nylon filter. Differences in S and SO42- estimates may indicate the presence of organosulfur (OS) species or biases between sampling and analytical methods. To reduce potential biases, an inductively coupled plasma-optical emission spectroscopy (ICP-OES) method was developed to allow for analysis of SO42- and S from a single filter extract. Sulfur (ICP-OES) and SO42- (IC) estimates from 2016 IMPROVE filters correlated strongly, suggesting that, on average, ICP-OES accurately estimated S. However, observed differences between slopes suggested the presence of water-soluble OS species, especially during summer. Organosulfur species are important indicators of secondary organic aerosols formed through reactions of biogenic and anthropogenic pollutants and can be quantified through laboratory techniques such as reverse-phase liquid chromatography (RPLC) or hydrophilic liquid interaction chromatography (HILIC) coupled to electrospray ionization-high-resolution tandem mass spectrometry (RPLC/ESI-HR-MS/MS and HILIC/ESI-HR-MS/MS, respectively), and field techniques using Aerodyne aerosol mass spectrometry (AMS). However, these methods are costly and introduce relatively large uncertainties when scaled for large networks such as IMPROVE. The method described in this report provides an inexpensive complement to XRF, which measures total S (insoluble and water-soluble S) to estimate water-soluble S and OS concentrations in PM.

The Maximum Likelihood Ensemble Filter with State Space Localization

Abstract: New method for ensemble data assimilation that incorporates state space covariance localization, global numerical optimization, and implied Bayesian inference, is presented. The method is referred to as the MLEF with State Space Localization (MLEF-SSL) due to its similarity with the Maximum Likelihood Ensemble Filter (MLEF). One of the novelties introduced in MLEF-SSL is the calculation of a reduced-rank localized forecast error covariance using random projection. The Hessian preconditioning is accomplished via Cholesky decomposition of the Hessian matrix, accompanied with solving triangular system of equations instead of directly inverting matrices. For ensemble update the MLEF-SSL system employs resampling of posterior perturbations. The MLEF-SSL was applied to Lorenz model II and compared to Ensemble Kalman Filter with state space localization and to MLEF with observation space localization. The observations include linear and nonlinear observation operators, each applied to integrated and point observations. Results indicate improved performance of MLEF-SSL, particularly in assimilation of integrated nonlinear observations. Resampling of posterior perturbations for ensemble update also indicates a satisfactory performance. Additional experiments were conducted to examine the sensitivity of the method to the rank of random matrix and to compare it to truncated eigenvectors of the localization matrix. The two methods are comparable in application to low-dimensional Lorenz model, except that the new method outperforms the truncated eigenvector method in case of severe rank reduction. The random basis method is simple to implement and may be more promising for realistic high-dimensional applications.

Inter-comparison of snow depth over sea ice from multiple methods

Abstract: . In this study, we compare eight recently developed snow depth products that use satellite observations, modeling or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance buoys (IMBs), snow buoys, snow depth derived from NASA's Operation IceBridge (OIB) flights, as well as snow depth climatology from historical observations. Large snow depth discrepancies between the different snow depth data sets are observed over the Atlantic and Canadian Arctic sectors. Among the products evaluated, the University of Washington snow depth product (UW) produces the overall deepest spring (March-April) snow packs, while the snow product from the Danish Meteorological Institute (DMI) provide the shallowest spring snow depths. There is no significant trend in the mean snow depth among all snow products since the 2000s, despite the great differences in regional snow depth. Two products, SnowModel-LG and the NASA Eulerian Snow on Sea Ice Model (NESOSIM), also provide estimates of snow density. Arctic-wide, these density products show the expected seasonal evolution with varying inter-annual variability, and no significant trend since the 2000s. The snow density in SnowModel-LG is generally higher than climatology, whereas NESOSIM density is generally lower. Both SnowModel-LG and NESOSIM densities have a larger seasonal change than climatology. Inconsistencies in the reconstructed snow parameters among the products, as well as differences between in-situ and airborne observations can in part be attributed to differences in effective footprint and spatial/temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in-situ and remote sensing observations.

Cloud Parameters and Temperature Profile Retrieval from Infrared Sounder Data.

Abstract: An inversion method is applied to the remote sensing of atmospheric temperature profiles in the presence of clouds. The method simultaneously calculates cloud-top pressure, amount and spectral emissivity, along with the temperature profile retrieval. Numerical analyses and retrieval experiments are carried out by using simulated sounder data. The sensitivity of the computed radiances to measurement noise and numerical errors is also examined. The retrieval results are physically discussed and numerically compared with the model atmospheric profiles. Comparisons reveal that the noise effect is more pronounced for thinner and/or smaller fractional cloud cases. It is also noted that the cloud thickness variation has a slight effect on temperature retrieval. Experiments on the proposed algorithm are carried out utilizing the High Resolution Infrared Sounder (HIRS) data of NOAA-6 and TIROS-N. Although the results of the experiments are difficult to verify quantitatively, the retrieved cloud cover and ...