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The growing body of Precise Orbit Determination data from emerging Low Earth Orbit constellations offers a promising source of thermospheric mass density measurements. Realising this potential requires the application of methods which can convert Precise Orbit Determination data into reliable density observations. This work provides an assessment of the Energy Dissipation Rate technique. Using a simulated constellation of up to eighteen Starlink-like satellites, we quantify the relationship between the quality of retrieved mass densities and four major influencing factors: Precise Orbit Determination noise level, drag acceleration magnitude, estimation fit-span, and the number of co-orbital spacecraft whose independent density estimates are averaged. Densities retrieved via the Energy Dissipation Rate method are also compared with densities that have been assimilated into a background density model. We identify conditions under which non-assimilated retrievals can outperform their assimilated counterparts, demonstrating the benefits of averaging data from multiple satellites along the same orbital track. Our results reveal three rules of thumb that describe how retrieval error scales as a function of fit-span, satellite count, and drag level. Doubling the fit-span roughly halves the RMS percentage error; adding co-orbital satellites reduces error as\n \n \n $${\\sim }N^{-0.6}$$<\/jats:tex-math>\n \n \n \u223c<\/mml:mo>\n \n N<\/mml:mi>\n \n -<\/mml:mo>\n 0.6<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n (unassimilated) or\n \n \n $${\\sim }N^{-0.25}$$<\/jats:tex-math>\n \n \n \u223c<\/mml:mo>\n \n N<\/mml:mi>\n \n -<\/mml:mo>\n 0.25<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n (assimilated); and drag-acceleration sensitivity follows a logistic response with the steepest gains between\n \n \n $$2\\times 10^{-8}$$<\/jats:tex-math>\n \n \n 2<\/mml:mn>\n \u00d7<\/mml:mo>\n \n 10<\/mml:mn>\n \n -<\/mml:mo>\n 8<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n and\n \n \n $$2\\times 10^{-7}\\,\\mathrm {m\\,s^{-2}}$$<\/jats:tex-math>\n \n \n 2<\/mml:mn>\n \u00d7<\/mml:mo>\n \n 10<\/mml:mn>\n \n -<\/mml:mo>\n 7<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msup>\n \n \n m<\/mml:mi>\n \n \n s<\/mml:mi>\n \n -<\/mml:mo>\n 2<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n . Together, these findings provide the first systematic quantification of how density-retrieval errors scale with Precise Orbit Determination noise, drag-acceleration level, fit-span, and satellite count. The resulting analytical approximation captures over 89% of the variance in simulated retrieval errors and is intended to support the design of next-generation assimilative thermospheric mass density models.\n <\/jats:p>","DOI":"10.1007\/s40295-026-00636-5","type":"journal-article","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T20:10:35Z","timestamp":1792699835000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bring The Noise: Three Rules for Improving Thermospheric Density Retrieval From LEO POD Data"],"prefix":"10.1007","volume":"73","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8981-2522","authenticated-orcid":false,"given":"Charles","family":"Constant","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaylah M.","family":"Mutschler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Santosh","family":"Bhattarai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"Pilinski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,22]]},"reference":[{"issue":"2019","key":"636_CR1","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.actaastro.2018.04.023","volume":"148","author":"S Alfano","year":"2018","unstructured":"Alfano, S., Oltrogge, D.: Probability of Collision: valuation, variability, visualization, and validity. 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