The District Court for the District of Columbia relied on the en banc decision in Critical Mass to hold that "impairment of the effectiveness of a government program is a proper factor for consideration in conducting an analysis under" Exemption 4. (472) The court utilized that test in a case involving a request for royalty rate information contained in licensing agreements that NIH entered into with pharmaceutical companies in accordance with a statutory mandate "to use the patent system to promote inventions arising from federally supported research." (473) The court upheld NIH's determination that it "'would cease to be an attractive or viable licensor of patented technology'" were it to disclose the royalty rate information. (474) The court found that "[s]uch a result obviously would hinder the agency in fulfilling its statutory mandate," and accordingly it afforded protection under the third prong of Exemption 4. (475) That same court issued a similar ruling in a case involving export-insurance documents, finding that disclosure "would interfere with the [Export-Import] Bank's ability to promote U.S. exports, and result in loss of business for U.S. exporters," which in turn would interfere with the agency's "ability to carry out its statutory purpose" of promoting the exchange of goods between the United States and foreign countries. (476)
Sources: EPI analysis of the Fair Labor Standards Act and amendments and the Raise the Wage Act of 2021. Total economy productivity data from the Bureau of Labor Statistics Labor Productivity and Costs program.
TOP Hourly Analysis Program 491 Serial Key 254
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Several studies have performed evaluations of the stage IV product but for limited spatial and/or temporal extents. Wang et al. (2008) used gauges to compare the stage IV product with the RFC specific stage III product for one basin in Texas. The main finding for this basin was that the Multisensor Precipitation Estimation (MPE) has a higher capability of rain detection than do gauges or stage III data. Westcott et al. (2008) compared monthly gauges amounts to the stage IV product and found that stage IV overestimates precipitation at the low end and underestimates precipitation at the high end. Habib et al. (2009a) performed a validation of the stage IV product using a dense network of gauges over Louisiana at small spatial and temporal scales. Nelson et al. (2010) used stage IV data for comparison to a reanalysis product over the southeastern United States. They found that the added value of quality control of the input radar-only products and gauge data provided better estimates of multisensor precipitation as compared to stage IV. Wu et al. (2012) evaluated the National Mosaic and Multisensor Quantitative (NMQ) Precipitation Estimation System (Zhang et al. 2011) for two seasons during 2009 using gauges and the stage IV product as supplementary information. They found some improved statistics for the NMQ versus the stage IV products but the 6-hourly stage IV product has a higher correlation coefficient than the 1-h stage IV product. Habib et al. (2013) evaluated six different products from the NWS MPE algorithm over Louisiana using rain gauges. They conclude that the most effective improvement in the rainfall products comes from applying the mean-field bias adjustment to the radar-only product. Hou et al. (2014) describe a methodology for generating a new dataset by adjusting 6-h accumulations of the stage IV data to the NWS Climate Prediction Center (CPC) unified gauge estimates. (Chen et al. 2008) The method has some limitations with heavy-to-extreme precipitation. Although there are many studies evaluating the stage IV product, these studies are limited in their scope spatially and/or temporally. To date, no comprehensive assessment of the product exists.
The 24-h stage IV product reprocessed 24 h after the verification time is the most accurate product for postanalysis of precipitation data. Use of the stage IV products in real time is not optimal as the hourly and 6-hourly maps are generated at differing times and for different reasons. For example, RFCs generate hourly and 6-hourly analyses first in an automated sense with no manual quality control. Then, the analysis is done several hours later in manual mode with quality control performed by a human analyst. So NCEP generates the national mosaic of hourly, 6-hourly, and daily mosaics at top-of-the-hour (i.e., approximately 30-min delay) periods with some RFCs sending their RMPAs at varying time periods. Therefore, a complete CONUS-wide map may not be available until several hours later. NCEP also produces a 24-h analysis that is the summation of the 6-hourly analysis. In addition, at least one RFC does not send hourly RMPA (Northwest RFC) to NCEP for processing. They only send the 6-h RMPA. It is also important to note that the 6-hourly analysis files are not the accumulation of the hourly analysis. The RFCs send hourly and 6-hourly analysis separately. The hourly RMPAs that NCEP receives are not manually quality controlled. However, the 6-hourly analysis that is included in the NCEP stage IV are the precipitation maps that are manually quality controlled at the RFC. RFCs do quality control hourly maps but at a time later than those that are sent to NCEP, and sometimes it happens that NCEP is not able to include the manually quality controlled hourly maps from every RFC in the NCEP stage IV hourly estimates.
Verification of the NCEP stage IV precipitation product is difficult for many reasons including but not limited to the availability of a consistent long-term CONUS-wide rain gauge dataset. For the purposes of verifying the stage IV precipitation product over the CONUS, we use the U.S. Climate Reference Network (USCRN). The USCRN is a network that was implemented in response to the challenges of siting a climate network that will provide no changes in the station history. The USCRN program aims to create a set of station records that provide a robust multidecadal climate monitoring capability (Diamond et al. 2013). As related to precipitation, the USCRN has three independent vibrating-wire weighing transducers that suspend the precipitation-bucket cradle and provide three independent measurements of the depth of the precipitation that has fallen in the bucket. Data are quality controlled and are available at the 5-min, hourly, daily, and monthly scales, in local standard time. For this study we use USCRN daily precipitation amounts accumulated from the hourly precipitation data with a conversion for local standard time to UTC to match the stage IV precipitation product. Figure 1 shows the distribution of gauges across the CONUS. While there is some sparse coverage over certain RFCs, we consider this to be the only in situ dataset that is available for CONUS-wide comparisons. Another widely used CONUS wide rain gauge network, the Global Historical Climatology Network (GHCN; Menne et al. 2012), provides a much denser network at the daily time scale but an investigation of this network showed that some of these sites are used in some capacity in the processing of the RFC-wide precipitation data that are sent to NCEP as inputs to stage IV. The specific gauge locations that are used are not known and thus it is difficult to extract those that could be used for verification. Thus, for verification purposes we use all available data from the USCRN precipitation observations to make a comparison of daily precipitation. The period of record of the USCRN station that is used for comparison varies from gauge to gauge based on when it was installed. Some gauges were installed in 2002 or prior and have a full record for the study period and, others were installed later (some as late as 2010) and thus have a shorter record.
In this paper, we provide an overview of the NCEP stage IV precipitation product. The product consists of hourly, 6-hourly, and 24-hourly maps of precipitation at the 4 km 4 km scale. In this analysis we only evaluate the 24-hourly maps of precipitation. The maps of precipitation are generated by the NCEP using a mosaicking technique that combines data from the 12 RFCs in the CONUS. We have provided an overview of the NWS precipitation processing system that generates quantitative precipitation estimates at the RFCs. The stage IV product is currently the only operational product that provides high-resolution radar-based precipitation estimates over the CONUS, and thus is used in many studies for comparison of precipitation products (i.e., satellite QPE). Our findings indicate that the stage IV product could be useful for certain types of studies but should be used with caution in other types of studies. We outline the strengths and weaknesses here. 2ff7e9595c
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