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Grupo de Estudantes

Público·19 membros
Jacob Merkushev
Jacob Merkushev

LS LAND Issue11 21 |WORK|

Hits (blue), misses (orange), false alarms (yellow), and correct negatives (purple) for each SPP over the (left) West, (center) Gulf, and (right) East Coasts for the (top) land, (middle) coast, and (bottom) ocean surface types.

LS LAND Issue11 21

As mentioned previously, the dataset is divided into three different surface types namely, land, coast, and ocean using the GPROFV05 surface classification dataset. The sample size of the matched dataset with and without quality control for each surface type is presented in Table 2.

Detection performance is evaluated with categorical skill scores: percentage of hits, misses, false and correct negatives are presented in Fig. 3. The results are broken down by regions (West Coast, Gulf of Mexico, and East Coast) and surface types (land, coast, and ocean surface types) and shown for retrievals IMERG Final (IM-F), IMERG Late (IM-L), IMERG Early (IM-E), IMERG PMW (PMW), IMERG IR (IR), IMERG PMW morph (morph), and morph and IR mix (morph+IR). The detection performance is discussed in terms of algorithms, regions, and surface types.

Surface types represent another conditioning factor for rainfall detection by IMERG (Fig. 3). Across all regions and products there is a tendency for lower hits over coastal surfaces. The coastal transitions remain a challenge for rainfall estimation from space. Misses are slightly more prevalent over land, reflecting the challenges in distinguishing the rainfall signal from the radiometrically warm and variable land surface. Surprisingly, false alarms tend to be higher over the ocean than over land.

Detection of stratiform and convective rainfall occurrence varies significantly across regions, which highlights the impact of precipitation regimes. Detection of precipitation types varies also slightly depending on surfaces. Over the land and coastal surfaces of the East Coast, all products detect convective rainfall occurrence slightly better than stratiform rainfall occurrence. It tends to be the other way around on the West Coast, where slightly higher hit rates are associated with stratiform rainfall. For example, the West Coast IM-F, IM-L, and IM-E stratiform hit rates are slightly higher (6%) over coastal and ocean surface types relative to the convective hit rate. Again, it probably reflects the difference in precipitation generation mechanisms across both regions, and the challenges in detecting orographic precipitation from space on the West Coast. Over the ocean, most products display similar or slightly better detection of stratiform rainfall (except IR). On the East Coast and West Coast, the detection of stratiform rainfall is higher over ocean than over land and coastal surfaces (+11% hits with IM-F on the West Coast), as expected. The detection of convective rainfall occurrence follows the same trend, albeit with less difference across surfaces. It is likely that the transition of surfaces and environments (e.g., surface emissivity gradients) impacts more stratiform than convective satellite estimates. On the Gulf Coast, the detection performance of both convective and stratiform precipitation remains about the same across surfaces for both precipitation types.

The three regions are characterized by different precipitation regimes and generation mechanisms. Accordingly, all products show regionally varying skill at delineating stratiform and convective GV-MRMS precipitation. Again, West Coast stands out with the highest detection performance difference between stratiform (higher) and convective (lower) rainfall types over land and coastal surfaces. West Coast overland detection of convective rainfall is the lowest across regions and surfaces. It confirms that the challenges in this region are associated with orographic generation of convective precipitation. Detection skills are generally more uniform across stratiform than convective precipitation over the ocean.

Coastal transitions remain a challenge for rainfall estimation. Misses are slightly more prevalent over land, reflecting the challenges in distinguishing the rainfall signal from the radiometrically warm and variable land surface. Moreover, across all regions and products there is a tendency for lower hit rates and higher false alarms over coastal surfaces.

Results: Core Treatments for Knee OA included arthritis education and structured land-based exercise programs with or without dietary weight management. Core Treatments for Hip and Polyarticular OA included arthritis education and structured land-based exercise programs. Topical non-steroidal anti-inflammatory drugs (NSAIDs) were strongly recommended for individuals with Knee OA (Level 1A). For individuals with gastrointestinal comorbidities, COX-2 inhibitors were Level 1B and NSAIDs with proton pump inhibitors Level 2. For individuals with cardiovascular comorbidities or frailty, use of any oral NSAID was not recommended. Intra-articular (IA) corticosteroids, IA hyaluronic acid, and aquatic exercise were Level 1B/Level 2 treatments for Knee OA, dependent upon comorbidity status, but were not recommended for individuals with Hip or Polyarticular OA. The use of Acetaminophen/Paracetamol (APAP) was conditionally not recommended (Level 4A and 4B), and the use of oral and transdermal opioids was strongly not recommended (Level 5). A treatment algorithm was constructed in order to guide clinical decision-making for a variety of patient profiles, using recommended treatments as input for each decision node.

TROPOMI is the single payload aboard the Sentinel 5 Precursor (S5P) satellite that has a sun-synchronous orbit with a local overpass time of approximately 13:30 with a near-daily global coverage since April 2018 (Veefkind et al., 2012). The TROPOMI NO2 retrieval algorithm is developed by the Royal Netherlands Meteorological Institute and based on the NO2 DOMINO algorithm with significant improvements including the improved retrieval of slant column density and spectral fitting (Boersma et al., 2018; Lorente et al., 2017; Van Geffen et al., 2015). TROPOMI retrieves tropospheric NO2 with a pixel size of 7 km 3.5 km at nadir, and the resolution has been improved to 5.5 km 3.5 km with a change in the S5P operation scenario since August 6, 2019 (orbit 9388) (Eskes and Eichmann, 2019). We only use TROPOMI offline observations with cloud coverage less than 0.3, and quality assurance greater than 0.75 (Eskes and Eichmann, 2019).