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.

Quality controls, bias, and seasonality of CO 2 columns in the boreal forest with Orbiting Carbon Observatory-2, Total Carbon Column Observing Network, and EM27/SUN measurements

Abstract: . Seasonal CO2 exchange in the boreal forest plays an important role in the global carbon budget and in driving interannual variability in seasonal cycles of atmospheric CO2 . Satellite-based observations from polar orbiting satellites like the Orbiting Carbon Observatory-2 (OCO-2) offer an opportunity to characterize boreal forest seasonal cycles across longitudes with a spatially and temporally rich data set, but data quality controls and biases still require vetting at high latitudes. With the objective of improving data availability at northern, terrestrial high latitudes, this study evaluates quality control methods and biases of OCO-2 retrievals of atmospheric column-averaged dry air mole fractions of CO2 ( X CO 2 ) in boreal forest regions. In addition to the standard quality control (QC) filters recommended for the Atmospheric Carbon Observations from Space (ACOS) B8 (B8 QC) and ACOS B9 (B9 QC) OCO-2 retrievals, a third set of quality control filters were specifically tailored to boreal forest observations (boreal QC) with the goal of increasing data availability at high latitudes without sacrificing data quality. Ground-based reference measurements of X CO 2 include observations from two sites in the Total Carbon Column Observing Network (TCCON) at East Trout Lake, Saskatchewan, Canada, and Sodankyla, Finland. OCO-2 retrievals were also compared to ground-based observations from two Bruker EM27/SUN Fourier transform infrared spectrometers (FTSs) at Fairbanks, Alaska, USA. The EM27/SUN spectrometers that were deployed in Fairbanks were carefully monitored for instrument performance and were bias corrected to TCCON using observations at the Caltech TCCON site. The B9 QC were found to pass approximately twice as many OCO-2 retrievals over land north of 50 ∘ N than the B8 QC, and the boreal QC were found to pass approximately twice as many retrievals in May, August, and September as the B9 QC. While boreal QC results in a substantial increase in passable retrievals, this is accompanied by increases in the standard deviations in biases at boreal forest sites from ∼1.4 parts per million (ppm) with B9 QC to ∼1.6 ppm with boreal QC. Total average biases for coincident OCO-2 retrievals at the three sites considered did not consistently increase or decrease with different QC methods, and instead, responses to changes in QC varied according to site and satellite viewing geometries. Regardless of the quality control method used, seasonal variability in biases was observed, and this variability was more pronounced at Sodankyla and East Trout Lake than at Fairbanks. Long-term coincident observations from TCCON, EM27/SUN, and satellites from multiple locations would be necessary to determine whether the reduced seasonal variability in bias at Fairbanks is due to geography or instrumentation. Monthly average biases generally varied between −1 and +1 ppm at the three sites considered, with more negative biases in spring (March, April, and May – MAM) and autumn (September and October – SO) but more positive biases in the summer months (June, July, and August – JJA). Monthly standard deviations in biases ranged from approximately 1.0 to 2.0 ppm and did not exhibit strong seasonal dependence, apart from exceptionally high standard deviation observed with all three QC methods at Sodankyla in June. There was no evidence found to suggest that seasonal variability in bias is a direct result of air mass dependence in ground-based retrievals or of proximity bias from coincidence criteria, but there were a number of retrieval parameters used as quality control filters that exhibit seasonality and could contribute to seasonal dependence in OCO-2 bias. Furthermore, it was found that OCO-2 retrievals of X CO 2 without the standard OCO-2 bias correction exhibit almost no perceptible seasonal dependence in average monthly bias at these boreal forest sites, suggesting that seasonal variability in bias is introduced by the bias correction. Overall, we found that modified quality controls can allow for significant increases in passable OCO-2 retrievals with only marginal compromises in data quality, but seasonal dependence in biases still warrants further exploration.

Snowfall and snow accumulation processes during the MOSAiC winter and spring season

Abstract: . Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation, and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth (HS) and density retrievals from a SnowMicroPen (SMP) and approximately weekly-measured snow depths along fixed transect paths. Hence, the computed SWE considers surface heterogeneities over an average path length of 1469 m. We used the SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were compared with ERA5 reanalysis snowfall rates for the drift track. Our study shows that the simple fitted HS-SWE function can well be used to compute SWE along a transect path based on SMP SWE retrievals and snow-depth measurements. We found an accumulated snow mass of 34 mm SWE until 26 April 2020. Further, we found that the Vaisala Present Weather Detector 22 (PWD22), installed on a railing on the top deck of research vessel Polarstern was least affected by blowing snow and showed good agreements with SWE retrievals along the transect, however, it also systematically underestimated snowfall. The OTT Pluvio2 and the OTT Parsivel2 were largely affected by wind and blowing snow, leading to higher measured precipitation rates, but when eliminating drifting snow periods, especially the OTT Pluvio2 shows good agreements with ground measurements. A comparison with ERA5 snowfall data reveals a good timing of the snowfall events and good agreement with ground measurements but also a tendency towards overestimation. Retrieved snowfall from the ship-based Ka-band ARM Zenith Radar (KAZR) shows good agreements with SWE of the snow cover and comparable differences as ERA5. Assuming the KAZR derived snowfall as an upper limit and PWD22 as a lower limit of a cumulative snowfall range, we estimate 72 to 107 mm measured between 31 October 2019 and 26 April 2020. For the same period, we estimate the precipitation mass loss along the transect due to erosion and sublimation as between 53 and 68 %. Until 7 May 2020, we suggest a cumulative snowfall of 98–114 mm.

Cloud-Base Height Estimation from VIIRS. Part I: Operational Algorithm Validation against CloudSat

Abstract: The operational VIIRS cloud-base height (CBH) product from the Suomi–National Polar-Orbiting Partnership (SNPP) satellite is compared against observations of CBH from the cloud profiling radar (CPR) on board CloudSat. Because of the orbits of SNPP and CloudSat, these instruments provide nearly simultaneous observations of the same locations on Earth for a ~4.5-h period every 2–3 days. The methodology by which VIIRS and CloudSat observations are spatially and temporally matched is outlined. Based on four 1-month evaluation periods representing each season from June 2014 to April 2015, statistics related to the VIIRS CBH retrieval performance have been collected. Results indicate that when compared against CloudSat, the VIIRS CBH retrieval does not meet the error specifications set by the Joint Polar Satellite System (JPSS) program, with a root-mean-square error (RMSE) of 3.7 km for all clouds globally. More than half of all matching VIIRS pixels and CloudSat profiles have CBH errors exceeding the 2...

Validation of OCO-2 error analysis using simulated retrievals

Abstract: . Characterization of errors and sensitivity in remotely sensed observations of greenhouse gases is necessary for their use in estimating regional-scale fluxes. We analyze 15 orbits of the simulated Orbiting Carbon Observatory-2 (OCO-2) with the Atmospheric Carbon Observations from Space (ACOS) retrieval, which utilizes an optimal estimation approach, to compare predicted versus actual errors in the retrieved CO2 state. We find that the nonlinearity in the retrieval system results in XCO2 errors of ∼0.9 ppm. The predicted measurement error (resulting from radiance measurement error), about 0.2 ppm, is accurate, and an upper bound on the smoothing error (resulting from imperfect sensitivity) is not more than 0.3 ppm greater than predicted. However, the predicted XCO2 interferent error (resulting from jointly retrieved parameters) is a factor of 4 larger than predicted. This results from some interferent parameter errors that are larger than predicted, as well as some interferent parameter errors that are more strongly correlated with XCO2 error than predicted by linear error estimation. Variations in the magnitude of CO2 Jacobians at different retrieved states, which vary similarly for the upper and lower partial columns, could explain the higher interferent errors. A related finding is that the error correlation within the CO2 profiles is less negative than predicted and that reducing the magnitude of the negative correlation between the upper and lower partial columns from −0.9 to −0.5 results in agreement between the predicted and actual XCO2 error. We additionally study how the postprocessing bias correction affects errors. The bias-corrected results found in the operational OCO-2 Lite product consist of linear modification of XCO2 based on specific retrieved values, such as the CO2 grad del ( δ ∇ CO 2 ), (“grad del” is a measure of the change in the profile shape versus the prior) and dP (the retrieved surface pressure minus the prior). We find similar linear relationships between XCO2 error and dP or δ ∇ CO 2 but see a very complex pattern of errors throughout the entire state vector. Possibilities for mitigating biases are proposed, though additional study is needed.

Objective Guidance for Use in Setting Tropical Cyclone Conditions of Readiness

Abstract: The Department of Defense uses a Tropical Cyclone Conditions of Readiness (TC-CORs) system to prepare bases and evacuate assets and personnel in advance of adverse weather associated with tropical cyclones (TCs) TC-CORs are recommended by weather facilities either on base or at central sites and generally are related to the timing and potential for destructive (50 kt; 1 kt ≈ 05144 m s−1) sustained winds Recommendations are then considered by base or area commanders along with other factors for setting the TC-CORs Ideally, the TC-CORs are set sequentially, from TC-COR IV (destructive winds within 72 h), through TC-COR III (destructive winds within 48 h) and TC-COR II (destructive winds within 24 h), and finally to TC-COR I (destructive winds within 12 h), if needed Each TC-COR, once set, initiates a series of preparations and actions Preparations for TC-COR IV can be as unobtrusive as obtaining emergency supplies, while preparations and actions leading up to TC-COR I are generally far more co